Environmental Assessment of

Proposed Effluent Limitations

Guidelines and Standards for

the Meat and Poultry Products

Industry Point Source

United States Office of Water EPA-821-B-01-008

Environmental Protection (4303) January 2002

Agency Washington, DC 20460
i

Table of Contents

Executive Summary ....................... ....................... ........... ES-1

1.0 Introduction ....................... ....................... ................... 1-1

1.1 Definition of MPP ....................... ....................... .......... 1-1

 1.2 Water Quality Issues Related to MPP ....................... .................. 1-1

 1.3 Potential Environmental Impacts of MPP ....................... ............... 1-2

 1.4 Organization of Report ....................... ....................... ...... 1-3

2.0 Methodology ....................... ....................... .................. 2-1

2.1 Introduction ....................... ....................... ............... 2-1

 2.2 Overview of Water Quality Assessment Approach ....................... ....... 2-1

2.2.1 Characterize Effluent Discharges ....................... ............. 2-3

2.2.2 Ensure MPP Survey Data is Model Read ....................... ....... 2-4

 2.3 Overview of NWPCAM .1.1 ....................... ....................... . 2-4

2.3.1 Types of Water Polluton Problems and Policies that Can Be Analyzed with 

NWPCAM ....................... ....................... ........ 2-5

 2.4 Pollutant Parameters Modeled Using NWPCAM 1.1 ....................... ...... 2-6

2.4.1 Dissolved Oxygen (DO) ....................... ..................... 2-6

2.4.2 Biochemical Oxygen Demand (BOD) ....................... .......... 2-6

2.4.3 Total Kjeldahl Nitrogen (TKN) ....................... ............... 2-8

2.4.4 Total Suspended Solids (TSS) ....................... ................ 2-9

2.4.5 Fecal Coliform Bacteria (FCB) ....................... ............... 2-9

2.4.6 Nutrients (Total Nitrogen and Total Phosphorus) ....................... 2-10

 2.5 Water Quality Modeling ....................... ....................... ... 2-11

 2.6 Water Use Support Determinations ....................... .................. 2-11

 2.7 Facility Effluent Data Inputs for NWPCAM ....................... ........... 2-14

 2.8 Model Runs ....................... ....................... ............. 2-14

 2.9 Creating Municipal and Industrial Select Tables ....................... ........ 2-16

 2.10 Direct Industrial Dischargers ....................... ....................... 2-16

 2.11 Indirect Industrial Dischargers ....................... ...................... 2-16

 2.12 POTWs ....................... ....................... ................. 2-17

3.0 Data Sources ....................... ....................... .................. 3-1

 3.1 Point Source Loads Used in NWPCAM to Estimate Baseline Water Quality Conditions . . 3-1

3.1.1 Municipal and Industrial Dischargers ....................... .......... 3-2

3.1.1.1 Primary Data Sources ....................... ................ 3-2

3.1.1.2 Typical Pollutant Concentrations ....................... ....... 3-4

3.1.1.3 Inventory of Point Source Facilities ....................... ..... 3-4

3.1.2 Urban Runoff and Combined Sewer Overflows ....................... . . 3-5

3.1.2.1 Primary Data Sources ....................... ................ 3-5
ii

3.1.2.2 Typical Pollutant Concentrations ....................... ....... 3-6

3.1.2.3 Primary Data Sources for Urban Runoff Estimates ................ 3-7

 3.2 Nonpoint Source Loads ....................... ....................... ...... 3-7

 3.3 Facility-Specific Loading Data ....................... ....................... 3-9

4.0 Results ....................... ....................... ....................... 4-1

 4.1 WQEA Results Summary ....................... ....................... .... 4-1

4.1.1 Treatment Options Modeled ....................... ................. 4-3

4.1.2 Facilities Modeled ....................... ....................... . . 4-4

4.1.3 Simplified Environmental Scale-up Factor ....................... ...... 4-4

4.1.4 Limitations of the WQEA ....................... ................... 4-5

 4.2 Documented Environmental Effects ....................... ................... 4-6

5.0 References ....................... ....................... .................... 5-1
iii

Table of Contents (continued)

APPENDICES

 A: Preamble Correction ....................... ....................... .... A-1

 B: Equations used to update municipal facility loadings ....................... . B-1

 C: Modules ....................... ....................... .............. C-1
iv

List of Tables

Page No.

Table ES-1 Regulatory Treatment Options....................... ................... ES-3

Table ES-2 Water Quality Criteria By Use ....................... .................. ES-4

Table ES-3 Empirical Calculation of Criteria from the Baseline Scenario ................. ES-6

Table ES-4 Benefit Scenarios Modeled (97 facilities) ....................... .......... ES-7

Table ES-5 Water Quality Index (WQI) Baseline and Proposed Treatment Level Statistics ... ES-8

Table 2-1 Regulatory Treatment Options....................... ..................... 2-3

Table 2-2 Water Quality Criteria Threshold By Use ....................... ........... 2-12

Table 2-3 Empirical Calculation of Criteria from the Baseline Scenario .................. 2-13

Table 2-4 Benefit Scenarios Modeled ....................... ...................... 2-15

Table 2-5 Fraction of Pollutant Retained as a Function of Treatment Level ................ 2-17

Table 2-6 Default Effluent Characteristics by Treatment Level ....................... . . 2-20

Table 3-1 Effluent Characteristics of rban Runoff and CSOs ....................... ..... 3-6

Table 3-2 National Summary of Annual Load Estimates for Urban and Rural Runoff and 

CSOs (as metric tons/day) ....................... ...................... 3-7

Table 4-1 Benefit Scenarios Modeled (97 facilities) ....................... ............ 4-2

Table 4-2 Water Quality Index (WQI) Baseline and Proposed Treatment Level Statistics ..... 4-3

Table 4-3 MPP Regulatory Treatment Options ....................... ................ 4-4

Table 4-4 Documented Environmental Effects of MPP Wastes on Water Quality ............ 4-8

Table IX.G-1 Modeled Environmental Benefits (97 facilities) ....................... ...... A-1
Environmental Assessment

of Proposed Effluent Limitations Guidelines

 and Standards for the

Meat and Poultry Products

Industry Point Source

January 2002

U.S. Environmental Protection Agency

Office of Science and Technology

Engineering and Analysis Division

1200 Pennsylvania Avenue, N.W.

Washington, D.C. 20460

Charles Tamulonis

Task Manager
ACKNOWLEDGMENTS AND DISCLAIMER

The Engineering and Analysis Division, of the Office of Science and Technology, has reviewed

and approved this report for publication. The Office of Science and Technology directed,

managed, and reviewed the work of Tetra Tech in preparing this report. Neither the United

States Government nor any of its employees, contractors, subcontractors (Research Triangle

Institute.), or their employees make any warranty, expressed or implied, or assumes any legal

liability or responsibility for any third party’s use of or the results of such use of any information,

apparatus, product, or process discussed in this report, or represents that its use by such party

would not infringe on privately owned rights.
ES - 1

EXECUTIVE SUMMARY

Purpose of the Water Quality Environmental Assessment (WQEA)

The purpose of the Water Quality Environmental Assessment (WQEA) is to estimate the

change in water quality conditions resulting from implementing an effluent guideline and

pretreatment standards for a given industry. This assessment performed by the U.S.

Environmental Protection Agency (EPA) as part of its effort to develop effluent limitations

guidelines and pretreatment standards for Meat and Poultry Processing Industry (MPP) facilities.

Definition of MPP

EPA defines the meat and poultry products (MPP) industry as facilities that slaughter

livestock (e.g., cattle, calves, hogs, sheep, and lambs), and/or poultry or process meat, and/or

poultry into products for further processing or sale to consumers. The industry is often divided

into three categories: (1) meat slaughtering and processing; (2) poultry slaughtering and

processing; and (3) rendering.

Water Quality Issues Related to MPP

The meat poultry processing industry (excluding rendering) uses an estimated 150 billion

gallons of water per year and ranks in the top third of all three digit SIC manufacturing sectors

with regard to overall water consumption. Water is used to clean the product, clean and sanitize

the production equipment, and to transport the waste away from the production area. Water can

also be used as a part of the process, such as in scalding birds to facilitate feather removal or

chilling the animal or meat to reduce its temperature. Although a portion of the water used by

this industry is reused and or recycled, most of the water becomes wastewater that is ultimately

discharged into the nation's waterways, either directly by the facility or indirectly though a

Publically Owned Treatment Works (POTW).

Potential Environmental Impacts of MPP

The untreated wastewater of MPP facilities contains high concentrations of biodegradable
ES - 2

dissolved organics, biochemical oxygen demand (BOD), total suspended solids (TSS), oil and

grease, pathogens, and nutrients such as nitrogen (including ammonia) and phosphorus. EPA's

sampling data collected from MPP facilities also found treatable concentrations of some metals

(e.g., copper and zinc). Some of these metals are fed to the animals as feed additives, and

therefore are assumed to be the source for these pollutants in the wastewater.

The discharge of high levels of biodegradable organics into receiving streams results in

increased microbial activity, as the microorganisms biodegrade these materials. Increases in

microbial activity associated with excessive nutrient loadings requires greater amounts of oxygen

than natural aeration processes can provide resulting in the decrease of available dissolved

oxygen (DO) for more complex aquatic organisms. This potential of a pollutant to remove

oxygen from receiving waters is called the biochemical oxygen demand (BOD). High

concentrations of BOD can reduce the DO content of waterbodies to levels insufficient to support

fish and invertebrates.

Habitat degradation can result from increased suspended particulate matter. Suspended

particulate matter reduces light penetration, and thus primary productivity. Accumulation of

suspended particles may also alter benthic spawning grounds and feeding habitats.

Nutrients, including phosphorus and nitrogen, are the primary causes of surface water

eutrophication, which can reduce dissolved oxygen content of waterbodies to levels insufficient

to support fish and invertebrates. Eutrophication may also increase the incidence of harmful algal

blooms that release toxins as they die and can severely affect wildlife, as well as humans.

Additionally, meat and poultry processing raw wastewaters contain significant amounts of

organic nitrogen which rapidly breaks down into ammonia. If left untreated, this poses a direct

toxicant to aquatic communities.

Oil and grease are known to produce toxic effects on aquatic organisms (e.g., fish,

crustacea, larvae and eggs, gastropods, bivalves, invertebrates, and flora). Pathogens are known
ES - 3

to impact a variety of water uses including recreation, drinking water sources, and aquatic life

and fisheries (Docket No. W-01-06, Record No. 10024 - Pathogen TMDL report). 

Treatment Options Modeled

EPA modeled four treatment options (see Table ES-1) for this analysis. Three of the

treatment options are for facilities which discharge directly to a water body. These facilities will

be referred to as direct dischargers. EPA designates the treatment options for existing direct

dischargers as Best Available Technology (BAT). One of the treatment options is for facilities

which discharge indirectly to a water body through a Publicly Owned Treatment Work (POTW).

These facilities will be referred to as indirect discharges. EPA designates the treatment options

for existing indirect dischargers as Pretreatment Standards for Existing Sources (PSES).

Table ES-1: Regulatory Treatment Options

Regulatory Option1 Technical Component

BAT2 Dissolved Air Flotation (DAF) (advanced oil/water separation),

Lagoon, and Disinfection (Oil and Grease, BOD5, TSS, Pathogen

removal) + Nitrification (NH3

 removal)

BAT3 BAT2 + Denitrification (Nitrate removal)

BAT4 BAT3 + (Phosphorus removal)

PSES1 DAF, Equalization (Oil and Grease, TSS, removal) phosphorus removal

1

BAT = Best Available Treatment PSES = Pretreatment Standards for Existing Sources

Facilities modeled

EPA had sufficient data to model 97 out of the 977 meat, poultry, and rendering facilities

that are in scope of the regulatory options evaluated for this proposed rule. To prepare for the

WQEA and a separate economic analysis, EPA mailed out 350 detailed surveys to generate both

environmental and economic data. EPA received 230 detailed surveys in time for data analysis of

this proposed rule making. Of the 230 detailed surveys, EPA received sufficient data to model
ES - 4

the environmental impacts of 97 facilities (36 direct dischargers and 61 indirect dischargers).

EPA did not evaluate 79 facilities with zero discharges or 54 facilities for which EPA had

insufficient data to conduct the water quality analysis.

Modeling Tools

EPA used the following tools for the WQEA:

• National Water Pollution Control Assessment Model (NWPCAM), version 1.1

• A modified Vaughn water quality ladder

EPA used NWPCAM was used to model the in-stream dissolved oxygen (DO)

concentration, as influenced by pollutant reductions of BOD, total Kjeldahl nitrogen (TKN), total

suspended solids (TSS) and fcal coliform bacteria (FCB). A detailed description of the

NWPCAM is presented in Section 2.0 Methodology. EPA used a modified Vaughn water

quality ladder to associate water quality and designated uses (Vaughn, unpublished). NWPCAM

1.1 compares the concentrations of BOD, TSS, FCB, and percent DO saturation (DOPCT) to

benchmark values associated with swimming, fishing, and boating uses (see Table ES-2). 

Table ES-2: Water Quality Criteria By Use

USE Supported Water Quality

Index (WQI)

Criteria

BOD5 TSS DOpct FCB

Swimming 99 1.5 10 83 200

Fishing 94.4 3 50 64 1000

Boating 79 4 100 45 2000

Note: BOD5 = 5 day Biochemical Oxygen Demand (mg/L)

TSS = Total Suspended Solids (JTU)

DOpct = percent of DO Saturation

FCB = Fecal Coliform Bacteria(MPN/100ml)
ES - 5

Swimming is associated with the most stringent water quality criteria, and boating is associated

with the least stringent water quality criteria. For BOD, TSS, and FCB, the in-stream

concentration must be below the benchmark value to attain a given use. For DOPCT, the in-stream

value must be greater than the benchmark value to meet criteria for that use. If even one

parameter does not meet its benchmark value, the stream is not considered to support that use. If

the stream reach does not attain any of the three uses, it is designated as "not supporting." 

This approach is somewhat problematic since water quality improvements are considered

to not occur unless movements result in migrating from one use category to another, e.g., fishing

to swimming. The implication of this is that the baseline condition for a given stream reach must

be just below the break point between categories so an improvement will result in the water

quality conditions moving beyond the breakpoint. Furthermore, this also implies that no within

use category movements have any value associated with them.. 

As an alternative to the stepwise ladder approach, EPA evaluated all water quality

changes. To accomplish this a continuous Water Quality Index (WQI) was constructed. The

WQI combines information from four water quality measures rather than using only the limiting

lowest quality criterion to define use category. For this benefit valuation, NWPCAM compiled a

WQI from turbidity, BOD, fecal coliforms, and dissolved oxygen indexes based on work by

McClelland (1974). Since the baseline distribution of use categories is well understood and

generally accepted, it is desirable for the distribution based on WQI to match the existing

distribution of use categories in the baseline. EPA derived WQI values to represent the

breakpoints on the water quality ladder based on empirical observation of the WQI distribution

among use categories in the baseline data. EPA calculated the mean and standard deviation of

WQIs for the reaches in each use category in the baseline population of reaches. If reaches are

normally distributed within each use category, 84 percent of observed WQI for each category

should be less than the mean WQI plus one standard deviation (SD). The Mean + SD value

serves as the criterion for the boundary with the next higher use category. Table ES-3 shows the

calculation and the resulting criteria. 
ES - 6

Table ES-3: Empirical Calculation of Criteria from the Baseline Scenario

Use Category

Mean

(WQI)

Standard Deviation

(WQI)

Criterion (Mean + SD)

(WQI)

No Use, 0 54.1 24.8 79.0

Boatable, 1 84.9 9.5 94.4

Fishable, 2 92.5 6.5 99.0

Swimmable, 3 98.5 2.3

Source: EPA analysis of Baseline Access database, 10/2/2001

Water Quality Environmental Assessment Results

EPA modeled the combined baseline loading of 49.9 million lb/yr for sample set of 97

MPP facilities. The baseline loadings consist of the following pollutants: bio-chemical oxygen

demand (BOD), total suspended solids (TSS), nitrogen, phosphorus, and total Kjeldahl nitrogen

(TKN). The results for the various combinations of BAT and PSES scenarios modeled are

presented in Table ES-4.

EPA estimates the preferred treatment option (Scenario 7) for this proposed rule would

reduce pollutant discharges from 36 MPP facilities by 4.8 million lb/yr. For this 10 percent

reduction, EPA estimates that this proposed rule would improve the WQI of 949 reach miles

(6,687 miles for the national set). The average WQI for these 949 reaches increases from 74.9 to

75.9 (see Table ES-5) which is still just below the boatable criteria breakpoint of WQI = 79. The

standard deviation suggest that 67 percent of the reaches impacted are located in the WQI range

of 60 - 92. 
ES - 7

Table ES - 4: Benefits Scenarios Modeled (97 facilities)1

Scenar io 2 Regulatory

Options 3

Pollutant 4

Load

(million

lbs/yr)

Pollutant

Reduction

(percent)

Step 5

 Improvement:

Overall use

(reach miles)

Contiuou s 6

Improvement:

Water Quality Index

(reach miles)

Sample 7 National 8 Sample National

Baseline 49.9

1 BAT2 47.5 5 17 116 926 6,325

2 BAT3 45.0 10 21 143 949 6,482

3 BAT4 44.8 10 21 143 952 6,502

4 BAT2 + PSES1 36.2 27 24 200 1216 9,799

5 BAT3 + PSES1 33.7 32 28 227 1240 9,968

6 BAT4 + PSES1 33.5 33 28 227 1244 10,000

7 BAT 3 (meat,

poultry), BAT2

(Rendering)

45.1 10 21 143 949 6,687

8 BAT 3 (meat,

poultry), BAT2

(Rendering) +

PSES1

33.7 32 28 227 1240 9,813

1 This table corrects several errors reported in preamble Table IX.G-1. For more information, please see Appendix A.

2 EPA is proposing Scenario 7 for the MPP effluent guideline rule making

3 BAT = Best Available Treatment (for Direct Discharges)

PSES= Pretreatment Standards for Existing Sources (for Indirect Dischargers)

4 Pound totals include BOD, TSS, Nitrogen, Phosphorus and TKN. Some overlap between categories may be occurring

5

Improvement credited when threshold conditions are met.

6

Improvement credited for any measurable change in water quality.

7 Sample set represents 97 facilities (36 direct and 61 indirect). 

8 National set represents 977 facilities (246 direct and 731 indirect).

In addition to estimating the continuous change in water quality, EPA also analyzed the

use category or step change approach. The reductions in loadings from the Scenario 7 with this

approach would result in the improved overall use of 21 reach miles (for the sample set), which

scales up to 143 reach miles (for the national set).
ES - 8

Table ES-5: Water Quality Index (WQI) Baseline and Proposed Treatment Level Statistics

Scenario Average

WQI 1

Standard

Deviation

(SD)

Mean + SD 2 Minimum Maximum 3

Baseline 74.9 16 59 - 91 6 99

BAT3 (meat, poultry),

BAT2 (Re ndering)

75.9 16.3 60 - 92 6 99

1 Boatable criteria is 79. Reaches with WQIs less than 79, are designated as supporting no use.

2 Represent the interval by which 67% of reaches are represented

Fishable criteria is 94.4.

3

Swimmable criteria is 99.

The large differences in miles of stream reaches affected attributable to these two

approaches is intuitively consistent: the continuous approach will count all stream reaches where

decreases in pollutants occur, whereas the use category approach will only estimate those

instances where a change in water quality results in migrating from one use category to another. 

Limitations of the WQEA

EPA believes that its analysis likely underestimates the potential benefits of the

regulatory options evaluated for this proposal. Specifically, the current version of the NWPCAM

model used for this environmental assessment (See Chapter 2) only models DO, BOD, Fecal

Coliform, TKN, and TSS. Accordingly, the analysis presented in today's proposal addresses only

a subset of MPP effluent contaminants. EPA intends to modify the model in support of the final

rule to include the following: (1) modeling of nutrients for an eutrophication analysis of ponds

and lakes; and (2) modeling of other pollutants for rivers and streams.

EPA did not evaluate the human health benefits associated with reduction of toxic

pollutant discharges, because MPP effluents do not contain significant levels of toxic

contaminants. Nonetheless, it is possible that MPP pollutants, especially nitrates, could have an

impact on certain human receptors, if contaminants reach drinking water supplies. 
ES - 9

EPA also did not evaluate the effects of MPP discharges on POTWs. MPP facilities

discharge mostly conventional pollutants (BOD, TSS, oil and grease, and fecal coliform

bacteria), which POTWs are designed to treat.
ES - 10
1 - 1

1.0 INTRODUCTION

The purpose of the Water Quality Environmental Assessment (WQEA) is to estimate the

change in water quality conditions resulting from implementing an effluent guideline and

pretreatment standards for a given industry.

This WQEA presents the results of the water quality assessment performed by the U.S.

Environmental Protection Agency (EPA) as part of its effort to develop effluent limitations

guidelines and pretreatment standards for Meat and Poultry Processing Industry (MPP) facilities.

1.1 DEFINITION OF MPP

EPA defines the meat and poultry products (MPP) industry as facilities that slaughter

livestock (e.g., cattle, calves, hogs, sheep, and lambs), and/or poultry or process meat, and/or

poultry into products for further processing or sale to consumers. The industry is often divided

into three categories: (1) meat slaughtering and processing; (2) poultry slaughtering and

processing; and (3) rendering.

1.2 WATER QUALITY ISSUES RELATED TO MPP

The meat poultry processing industry (excluding rendering) uses an estimated 150 billion

gallons of water per year and ranks in the top third of all three digit SIC manufacturing sectors

with regard to overall water consumption. Water is used to clean the product, clean and sanitize

the production equipment, and to transport the waste away from the production area. Water can

also be used as a part of the process, such as in scalding birds to facilitate feather removal or

chilling the animal or meat to reduce its temperature. Although a portion of the water used by

this industry is reused and or recycled, most of the water becomes wastewater that is ultimately

discharged into the nation's waterways, either directly by the facility or indirectly though a
1 - 2

Publically Owned Treatment Works (POTW).

1.3 POTENTIAL ENVIRONMENTAL IMPACTS OF MPP

The untreated wastewater of MPP facilities contains high concentrations of biodegrable

dissolved organics, biochemical oxygen demand (BOD), total suspended solids (TSS), oil and

grease, pathogens, and nutrients nitrogen (including ammonia) and phosphorus. EPA's sampling

data collected from MPP facilities also found treatable concentrations of some metals (e.g.,

copper and zinc). Some of these metals are fed to the animals as feed additives, and therefore are

assumed to be the source for these pollutants in the wastewater.

The discharge of high levels of biodegradable organics into receiving streams results in

increased microbial activity, as the microorganisms biodegrade these materials. This increase in

microbial activity requires greater amounts of oxygen than natural aeration processes can

provide. This deficit results in the decrease of available dissolved oxygen (DO) for more

complex aquatic organisms. This potential of a pollutant to remove oxygen from receiving waters

is called the biochemical oxygen demand (BOD). High concentrations of BOD can reduce the

DO content of waterbodies to levels insufficient to support fish and invertebrates.

Habitat degradation can result from increased suspended particulate matter. Suspended

particulate matter reduces light penetration, and thus primary productivity. Accumulation of

suspended particles may also alter benthic spawning grounds and feeding habitats.

Nutrients, including phosphorus and nitrogen, are the primary causes of surface water

eutrophication, which can reduce dissolved oxygen content of waterbodies to levels insufficient

to support fish and invertebrates. Eutrophication may also increase the incidence of harmful algal

blooms that release toxins as they die and can severely affect wildlife, as well as humans.

Additionally, meat and poultry processing raw wastewaters contain significant amounts of
1 - 3

organic nitrogen which rapidly breaks down into ammonia. If left untreated, this poses a direct

toxicant to aquatic communities.

Oil and grease are known to produce toxic effects on aquatic organisms (i.e., fish,

crustacea, larvae and eggs, gastropods, bivalves, invertebrates, and flora). Pathogens are known

to impact a variety of water uses including recreation, drinking water sources, and aquatic life

and fisheries (Docket No. W-01-06, Record No. 10024 - Pathogen TMDL report).

1.4 ORGANIZATION OF REPORT

EPA has organized this WQEA report into five sections. Section 2 outlines the

methodology EPA used to evaluate water quality effects from direct and indirect discharging

facilities. Section 3 describes the data sources used for evaluating water quality effects, such as

facility-specific data, water use category criteria, and documented environmental impact data.

Section 4 presents a summary of the results of this analysis. Section 5 provides a complete list of

references cited. Appendices B and C provide additional detail on the specific information

addressed in the main report.
1 - 4
1Direct discharge facilities are those which discharge effluent directly into water bodies,

usually following on-site wastewater treatment. Indirect discharge facilities are those which

discharge effluent into a publicly owned treatment works (POTWs), which provides subsequent

effluent treatment prior to discharge.

2 - 1

2.0 METHODOLOGY

2.1 INTRODUCTION

EPA evaluated potential water quality effects of discharges of conventional pollutants

from meat and poultry processing (MPP) facilities on receiving streams in a national analysis of

direct and indirect discharges1. Specifically, EPA used the National Water Pollution Control

Assessment Model (NWPCAM version 1.1) to model the change in reach specific instream

concentrations of total suspended solids (TSS), fecal coliform bacteria (FCB), ultimate

carbonaceous biochemical oxygen demand (CBODu), and dissolved oxygen (DO) concentration.

Total Kjeldahl nitrogen (TKN) was also modeled to account for oxygen depletion through

nitrogenous biochemical oxygen demand (NBOD). 

The modeled changes in these concentrations were then used to identify changes in use

categories ranging from the least desirable of no use, to boatable, to fishable, to the most

desirable of swimmable.

In the following sections, EPA presents the water quality assessment approach, the

NWPCAM model, the pollutants evaluated, and water use support determinations.

2.2 OVERVIEW OF WATER QUALITY ASSESSMENT APPROACH

The main purpose of the Water Quality Environmental Assessment (WQEA) is to

estimate changes in water quality conditions resulting from adoption of new limitations and
2 - 2

standards as established by the proposed rule. Developing these estimates involves a multi-step

process that begins with the analysis presented in the MPP Technical Development Document

(TDD) and ends with the MPP Economic Analysis. The first four steps, which are covered in the

TDD, are:

1. Identify the universe of MPP discharging facilities

2. Differentiate MPP direct dischargers from MPP indirect dischargers

3. Characterize the technology in place for each of the MPP facilities being

evaluated

4. Characterize effluent discharges from each of the MPP facilities being evaluated

with baseline and various regulatory options being proposed.

The next three steps, which are the focus of this WQEA, are:

5. Identify and characterize the receiving water body for each direct discharger, and

identify the associated POTW for each indirect discharger.

6. Estimate water quality conditions under current discharge conditions and under

regulatory alternatives.

7. Quantify changes in water use categories (i.e., fishable, boatable, swimmable)

The final step, covered in the Economic Analysis, is:

8. Monetize changes in environmental benefits associated with changes in water use

categories.

Even though the characterization of the MPP effluent discharges (step 4) is covered in the

MPP TDD, a quick summary of the process is presented below. The rest of Section 2 presents

EPA's methodology used to complete steps 5 through 7. 
2 - 3

2.2.1 Characterize Effluent Discharges

EPA estimated baseline and treatment option loadings on a facility-specific level. The

baseline loadings were based primarily on data provided in the 2001 MPP Detailed Survey.

Where data for specific pollutants could not be obtained from the survey or facility compliance

reports, EPA used surrogate data, so that each facility could be modeled for the full suite of

pollutants of concern addressed by NWPCAM.

EPA estimated the treatment option loadings for each facility, based upon expected

pollutant removals from implementation of the proposed effluent guideline. The evaluated

treatment options are presented in Table 2-1.

Table 2-1: Regulatory Treatment Options

Regulatory Option1 Technical Component

BAT2 Dissolved Air Flotation (DAF) (advanced oil/water separation),

Lagoon, and Disinfection (Oil and Grease, BOD5

, TSS, Pathogen

removal) + Nitrification (NH3 removal)

BAT3 BAT2 + Denitrification (Nitrate removal)

BAT4 BAT3 + (Phosphorus removal)

PSES1 DAF, Equalization (Oil and Grease, TSS, removal) phorus removal

1 BAT = Best Available Treatment (covers existing Direct Dischargers)

PSES = Pretreatment Standards for Existing Sources (covers existing Indirect Dischargers)

EPA only applied those BAT/PSES controls that would achieve pollutant concentration

levels at least equal to the facility's current treatment in place. Under this approach, a facility that

was characterized as having equivalent BAT2 technology in place would only obtain reduced

pollutant concentrations levels under more stringent technology options (i.e., BAT3 or BAT4

controls).
2 - 4

2.2.2 Ensure MPP Survey Data is Model Ready

EPA performed several additional steps to ensure that the facility information provided

through the MPP Survey, as well as the loadings data generated by the other analyses, could be

successfully entered into the NWPCAM model. For example, data on facility specific location

information provided in the survey had to be matched with NWPCAM location data for receiving

water bodies. In cases where Survey respondents did not identify discharge location (direct

dischargers) or associated POTW ( indirect dischargers), EPA staff made follow-up calls to the

facility operators to ensure that estimated discharges were allocated to the correct water body or

POTW, respectively. The Agency made additional modifications to the facility loadings data set

to ensure proper data formatting for use in the NWPCAM model. Once EPA entered the facility

loadings into the NWPCAM model, EPA combined facility impacts with existing stream reach

conditions to estimate water quality under both baseline and regulatory options.

2.3 OVERVIEW OF NWPCAM 1.1

NWPCAM is a national-level water quality modeling system and policy analysis tool. It

incorporates a national-scale water quality model into a system designed for conducting policy

simulations and benefits assessments. The core of NWPCAM is its water quality modeling

system. The system is built on a surface water routing framework that covers virtually the entire

inland region of the continental United States. This framework catalogs where surface waters are

located and how they are interconnected, and it characterizes the dimensions and flow of water

through this network. It is through this routing framework that the hydrological, hydrodynamic,

and surface water transport components of the system are integrated into NWPCAM.

A second major component of the modeling system is the pollutant loadings data. This

component defines the location and magnitude of discharges to the nationwide surface water

network for a selected number of conventional and nutrient pollutants. These loadings are

defined for both point and nonpoint sources of water pollution.

The kinetics component of the modeling system then incorporates information from the previous
2 - 5

components and simulates how the selected pollutants are dispersed and transformed throughout

the surface water network. The primary output of these integrated modeling components are

water quality estimates, primarily measured as in-stream pollutant concentrations, across this

network.

2.3.1 Types of water pollution problems and policies that can be analyzed with

NWPCAM

NWPCAM was originally developed and designed to conduct retrospective analyses of

Clean Water Act policies, but has been adapted for conducting prospective analyses of new or

proposed regulations. As the model has been expanded and refined, it has become suitable for

analyzing an increasingly diverse set of water pollution problems and policies.

Because of its large scale, the development of NWPCAM's water quality modeling and

policy evaluation system has been incremental. The scope of the model has been gradually

expanded to include more pollutants, more pollutant sources (point and nonpoint), more water

bodies, and more water quality measures. For instance, the first version of this model (the

CWAEM) incorporated only two "conventional" pollutants (CBODu and TSS), and it included

urban and rural nonpoint sources, municipal point sources, and "major" industrial point sources.

A subsequent version (NWPCAM Version 1.0) added modeling capability for two additional

conventional pollutants (FCB and DO) and added Combined Sewer Overflows (CSOs) and

approximately 20,000 "minor" industrial dischargers. The entire model has been reimplemented

from its original location on the EPA IBM mainframe running under SAS to a PC-based platform

under Microsoft Access.

NWPCAM 1.1 models four conventional pollutants: DO, CBOD, TSS, and FCB. TKN is

also included to support the modeling of DO and BOD. These pollutants have been the primary

focus of federal water pollution control policies under the Clean Water Act and have the

following advantages for modeling purposes:
2 - 6

• They can be characterized by first-order kinetics.

• Data is widely available to estimate point and nonpoint source loadings of these

constituents.

• Existing surface water quality indices are based, at least in part, on these

parameters.

For this analysis, NWPCAM 1.1 was further modified to also model total nitrogen and

total phosphorous. However, only the four conventional pollutants were employed in use support

determinations.

2.4 POLLUTANT PARAMETERS MODELED USING NWPCAM 1.1

2.4.1 Dissolved Oxygen (DO)

Levels of DO in surface water are commonly used as an indicator of aquatic health. High

levels of oxygen are characteristic of good water quality that can support a high-quality fishery

and diverse aquatic biota. Conversely, low or depleted oxygen concentrations indicate poor water

quality and an inability to support a diverse population of aquatic biota. DO is added to water

through photosynthesis and aeration from turbulent mixing, and is removed through respiration

and sediment oxygen demand.

In NWPCAM 1.1, oxygen production from photosynthesis (P) and consumption from

respiration (R) were assumed to balance (i.e., P = R or P – R = 0). Increases in DO concentration

due to atmospheric reaeration were accounted for by water temperature, velocity, and depth of

the river channel. The additional atmospheric oxygen that can be contributed to a free-flowing

stream falling over a dam or waterfall was not represented.

2.4.2 Biochemical Oxygen Demand (BOD)

Biodegradable organic materials, such as plant, fish, or animal matter, consume DO
2 - 7

during decomposition. The level of organics in wastewater and natural water bodies has

historically been assessed using BOD, which measures the pollutants' potential to remove oxygen

from the receiving waters. BOD is a primary determinant of DO concentrations in surface water.

Both the carbonaceous and nitrogenous components of the ultimate BOD (CBODU and

NBODU, respectively) are needed to model DO. The decomposition of organic carbon was

represented by the decay of CBODU as an oxygen equivalent measure of the amount of organic 

carbon. The labile/refractory and dissolved/particulate fractions of total organic carbon were not

differentiated. Eutrophication was not considered in this model, so the contributions of algal

respiration, algal mortality, and zooplankton grazing to organic carbon concentrations were also

not represented.

Loadings of CBODU occur from both point (e.g., municipal and industrial dischargers)

and nonpoint sources (e.g., urban runoff). Since effluent loading data for these sources are

typically characterized as the 5-day BOD or CBOD (BOD5 and CBOD5, respectively),

conversion factors were used to obtain CBODU for input to the model. BOD5 data obtained from

literature was assumed to represent CBOD5 because of uncertainty related to the interpretation of

BOD5 measurements (Hall and Foxen, 1984). The magnitude of the conversion factors for

municipal dischargers depend on treatment level as the relative proportion of easily degraded

materials in the effluent declines as the efficiency of waste treatment improves (Leo et al., 1984;

Thomann and Mueller, 1987).

The sequential nitrogen-cycle processes of hydrolysis of organic nitrogen to ammonia,

oxidation of ammonia to nitrite, and oxidation of nitrite to nitrate (nitrification) were simplified

by combining these steps into a single NBOD representation. Because organic nitrogen in

wastewater can be hydrolyzed to ammonia and thus contribute to the eventual oxygen demand in

a receiving water, the total NBOD is determined as the oxygen equivalent of the sum of organic

nitrogen and ammonia (see Equation 1). Total Kjeldahl nitrogen (TKN) represents the sum of
2 - 8

organic nitrogen and ammonia.

(1)

where:

O2/N = stoichiometric equivalent of 4.57 g oxygen per 1 g nitrogen consumed in the

stepwise nitrification process of ammonia to nitrate, and 

TKN = total Kjeldahl nitrogen.

Although the use of a lumped NBOD approach to account for the oxygen consumption

component of the nitrogen cycle has known shortcomings in representing the lag time needed to

initiate nitrification (Chapra, 1997), the approach adopted is consistent with other components of

the simplified model framework.

2.4.3 Total Kjeldahl Nitrogen (TKN)

Total Kjeldahl nitrogen (TKN) is the sum of organic nitrogen and ammonia. It is the key

pollutant in modeling DO. Sources of TKN include municipal and industrial discharges,

combined sewer overflows, and urban and rural runoff. It is routinely measured in water and

wastewater monitoring programs. Under aerobic conditions, nitrification occurs as described in

Section 2.1.2 and results in DO consumption. Under anaerobic conditions, the reverse process

(denitrification) occurs. During denitrification, nitrate is reduced to nitrite, nitrate is converted to

free nitrogen, and the free nitrogen is either assimilated by nitrogen-fixing, blue-green algae, or

released to the atmosphere as a gas.

In the absence of a national database to characterize benthic regeneration rates for

ammonia, a stoichiometric weight ratio for oxygen to nitrogen of 15.1:1 (Redfield, Ketchum, and

Richards, 1963) was used to define the equivalent amount of ammonia nitrogen released by

decomposition of organic carbon in the sediment bed. The benthic release of ammonia to the
2 - 9

water column was estimated from the reach-dependent parameter values assigned for sediment

oxygen demand (Di Toro, 1986; Di Toro et al., 1990).

2.4.4 Total Suspended Solids (TSS)

Total suspended solids (TSS) are used as a surrogate indicator of water transparency to

characterize recreational service flows provided by a water body. Low TSS concentrations are

associated with a high degree of water clarity. High concentrations of TSS are generally

associated with murky or turbid waters and are therefore important contributors to perceptions of

poor water quality. The assessment of economic benefits is, in part, dependent on changes in

water transparency (as assessed by TSS) and corresponding improvements that result from

implementing controls to reduce TSS loadings.

In NWPCAM 1.1, no distinction is made between the relative fractions of cohesive (clays

and silts) and noncohesive (sands) particle sizes that contribute to deposition processes from the

water column or the sediment bed concentration of solids that contributes to the resuspension of

solids back into the water column. A simple net settling velocity was used to parameterize the

interactions of particle size distributions with deposition and resuspension.

2.4.5 Fecal Coliform Bacteria (FCB)

In accordance with the practices of the EPA and other public health officials, the

NWPCAM 1.1 model uses FCB as a surrogate indicator for waterborne pathogens that are known

to cause a variety of human illnesses. Low densities of FCB are characteristic of good water

quality and low risk of waterborne diseases. High concentrations of FCB indicate poor water

quality and a high risk of waterborne diseases. Using typical water quality standards for primary

contact recreation (swimming) and secondary contact recreation (boating), the concentration of

FCB is directly related to service flows and economic benefits. The assessment of economic

benefits, in part, depends on changes in FCB concentrations and improvements in service flows

that may result from implementing controls to reduce FCB loadings.
2 - 10

FCB are introduced into natural waters by municipal and industrial wastewater

discharges, combined sewer overflows, and urban and rural runoff. Animal feedlots in rural areas

also contribute high loading rates of bacteria. High loading rates are most commonly associated

with untreated or poorly treated human sewage or animal waste. Bacteria are lost from the water

column by mortality, adsorption to particles, and settling. The mortality of coliform bacteria can

be functionally related to salt content, water temperature, and incident solar radiation (Mancini,

1978). In shallow waters, bacteria can be reintroduced back into the water column by

resuspension of particles under high flow conditions. In NWPCAM 1.1, the components of the

mortality and net settling loss rate for FCB were parameterized by a lumped

temperature-dependent net loss rate.

2.4.6 Nutrients (Total Nitrogen and Total Phosphorous)

Total nitrogen (TN) and total phosphorous (TP) are present in wastewater, surface runoff,

rainwater, groundwater, and surface waters. They exist in organic and inorganic forms and in

both dissolved and particulate fractions.

Elevated nutrient concentrations can affect a number of different water quality processes

and endpoints. Perhaps the most direct impact of nutrient loading is the toxicity of ammonia and

nitrate to aquatic and human populations. Ammonia, particularly the un-ionized form (NH3), is

highly toxic to aquatic organisms. Nitrates are considered a potential health concern for humans,

particularly for infants.

Excessive enrichment with phosphorous and/or nitrogen is also associated with the

process of cultural eutrophication, the anthropogenically induced acceleration of natural aging in

aquatic systems. Symptoms include shifts in ecological processes (e.g., carbon, nitrogen,

phosphorus, and oxygen cycling rates and dynamics) and plant and animal species. In the

extreme state of "hypereutrophy," a system may suffer extensive DO depletion, wide diurnal

shifts in oxygen corresponding with photosynthesis and respiration cycles, extreme bloom

events, noxious and undesirable algal species, fish kills, foul odors, and turbidity.
2 - 11

Total nitrogen and total phosphorous were modeled using first order kinetics. Decay

coefficients were obtained from the SPAtially Referenced Regressions On Watershed

(SPARROW) study (Smith, Schwarz, and Alexander, 1997).

As mentioned above, nutrient concentrations were not considered in use support

determinations. However, the development of regional nutrient criteria makes this a possibility

for the future. 

2.5 WATER QUALITY MODELING

NWPCAM 1.1 has the capability to model water quality in a stream reach after inclusion

of all source loadings. A change of loadings from one source is realistically considered in

comparison with the total loadings to a stream reach. NWPCAM 1.1 contains information on

approximately 8,000 municipal facilities and 23,000 industrial facilities. It can be run on any

scale, ranging from a basin to the entire United States.

Municipal and industrial select tables contain all loading information. Each discharger is

associated with identification information (name and NPDES number), receiving water (as

identified by CU, segment number, and distance along the stream reach), loading data (flow rate

and concentrations), and a sequence number to identify the order in which stream reaches are

modeled. The municipal and industrial select tables are generated each time the study area is

selected. After the model is run, each stream reach is associated with steady-state flow rate and

concentration information.

2.6 WATER USE SUPPORT DETERMINATIONS

In order to quantify the economic benefits associated with improved water quality, a
2 - 12

modified Vaughn water quality ladder is used to associate water quality and designated uses

(Vaughn, unpublished). NWPCAM 1.1 compares the concentrations of BOD5, TSS, FCB, and

percent DO saturation (DOPCT) to benchmark values associated with swimming, fishing, and

boating uses (see Table 2-2). Swimming is associated with the most stringent water quality

criteria, and boating is associated with the least stringent water quality criteria. For BOD5, TSS,

and FCB, the in-stream concentration must be below the benchmark value to attain a given use.

For DOPCT, the in-stream value must be greater than the benchmark value to meet the criterion for

that use. If even one parameter does not meet its benchmark value, the stream is not considered

to support that use. If the stream reach does not attain any of the three uses, it is designated as

"not supporting."

Table 2-2: Water Quality Criteria Threshold By Use

USE Supported Water Quality

Index (WQI)

Criteria

BOD5 TSS DOpct FCB

Swimming 99 1.5 10 83 200

Fishing 94.4 3 50 64 1000

Boating 79 4 100 45 2000

Note: BOD5 = 5 day Biochemical Oxygen Demand (mg/L)

TSS = Total Suspended Solids (mg/L)

DOpct = percent of DO Saturation

FCB = Fecal Coliform Bacteria(MPN/100ml)

This approach is somewhat problematic since water quality improvements are considered

to not occur unless movements result in migrating from one use category to another, e.g., fishing

to swimming. The implication of this is that the baseline condition for a given stream reach must

be just below the break point between categories so an improvement will result in the water

quality conditions moving beyond the breakpoint. Furthermore, this also implies that no within

use category movements have any value associated with them.. 
2 - 13

As an alternative to the stepwise ladder approach, EPA evaluated all water quality

changes. To accomplish this a continuous Water Quality Index (WQI) was constructed. The

WQI combines information from four water quality measures rather than using only the limiting

lowest quality criterion to define use category. For this benefit valuation, NWPCAM compiled a

WQI from turbidity, BOD, fecal coliforms, and dissolved oxygen indexes based on work by

McClelland (1974). Since the baseline distribution of use categories is well understood and

generally accepted, it is desirable for the distribution based on WQI to match the existing

distribution of use categories in the baseline. EPA derived WQI values to represent the

breakpoints on the water quality ladder based on empirical observation of the WQI distribution

among use categories in the baseline data. EPA calculated the mean and standard deviation of

WQIs for the reaches in each use category in the baseline population of reaches. If reaches are

normally distributed within each use category, 84 percent of observed WQI for each category

should be less than the mean WQI plus one standard deviation (SD). The Mean + SD value

serves as the criterion for the boundary with the next higher use category. Table 2-3 shows the

calculation and the resulting criteria. 

Table 2-3: Empirical Calculation of Criteria from the Baseline Scenario

Use Category

Mean

(WQI)

Standard Deviation

(WQI)

Criterion (Mean + SD)

(WQI)

No Use, 0 54.1 24.8 79.0

Boatable, 1 84.9 9.5 94.4

Fishable, 2 92.5 6.5 99.0

Swimmable, 3 98.5 2.3

Source: EPA analysis of Baseline Access database, 10/2/2001
2 - 14

2.7 FACILITY EFFLUENT DATA INPUTS FOR NWPCAM

Effluent data extracted from the MPP Detalied Surveys were entered into NWPCAM

model for the 97 meat-processing facilities evaluated for the benefit analysis. As described

below, some adjustments were required, because not all facilities collected data for all parameters

evaluated in the environmental assessment.

Each facility's effluent was characterized by flow rate and the six water quality

parameters discussed in section 2.0 (BOD5, TSS, FCB, TN, TKN, and TP). The current effluent

quality at each facility was defined at the "baseline average concentration" (BAC). Direct

discharge facilities were also associated with a maximum of three alternative effluent qualities

that would result upon implementation of a Best Available Technology (BAT) control. Indirect

discharge facilities were associated with one alternative effluent quality that would result upon

implementation of the Pretreatment Standard for Existing Sources (PSES).

The facility effluent data were modified prior to use in NWPCAM. The type of discharger

(i.e., direct or indirect) was changed for four industrial facilities based on the NWPCAM model.

Of the 97 total facilities, 36 were direct dischargers and 61 were indirect dischargers. When

facilities lacked data for one or more control options, the effluent quality was assumed to be

identical to the baseline average concentration. The original data contained some instances of

TKN concentrations that were larger than corresponding TN concentrations. In these instances,

TN was set equal to TKN. The modified data was reformatted and inserted into NWPCAM as

two tables (TTMunSelect and TTIndSelect).

2.8 MODEL RUNS

EPA performed nine model runs to estimate baseline conditions and water quality

changes for various combinations of regulatory controls. They correspond to the following
2 - 15

scenarios shown in Table 2-4.

BAT options were applied to direct dischargers and PSES options were applied to

indirect dischargers. It should be emphasized that differences between baseline loadings and

technology treatment options were calculated prior to input into NWPCAM. As described in

detail in the MPP Technical Development Document, facility loadings under alternative

technology options were derived based on technology performance of model facilities for each

MPP industry subcategory. The NWPCAM model used these estimates to model water quality

changes in affected receiving water bodies on a facility-by-facility basis.

Table 2-4: Benefits Scenarios Modeled

Model

Run

Regulatory Options 1

1 Scenario 0: Baseline

2 Scenario 1: BAT2 

3 Scenario 2: BAT3

4 Scenario 3: BAT4

5 Scenario 4: BAT2 + PSES1

6 Scenario 5: BAT3 + PSES1

7 Scenario 6: BAT4 + PSES1

8 Scenario 7: BAT3 (meat, poultry), BAT2 (Rendering)

9 Scenario 8: BAT3 (meat, poultry), BAT2 (Rendering) + PSES1

1 BAT: Best Available Treatment (for Direct Discharges)

BAT2: Dissolved Air Flotation (DAF) (advanced oil/water separation), Lagoon, and Disinfection (Oil and Grease,

BOD5

, TSS, Pathogen removal) + Nitrification (NH3

 removal)

BAT3: BAT2 + Denitrification (Nitrate removal)

BAT4: BAT3 + Phosphorus removal

PSES Pretreatment Standards for Existing Sources (for Indirect Dischargers)

PSES1: Dissolved Air Flotation (DAF) , Equalization (Oil and Grease, TSS removal) 
2 - 16

2.9 CREATING MUNICIPAL AND INDUSTRIAL SELECT TABLES

To perform the nine model runs, one municipal select and nine industrial select tables

were needed. The original NWPCAM model was run over the entire United States to generate

one municipal and one industrial select table containing information on the facilities originally

contained in NWPCAM. Nine copies were made of the industrial select table to correspond with

baseline and Model Runs 2-9. Records for 151 facilities (36 direct industrial dischargers, 61

indirect industrial dischargers, and 59 municipalities) were inserted or updated using the facility

specific data generated from the MPP Survey and other compliance reports. The specific

approach to update the loadings data was dependent on the facility type (i.e., direct industrial

discharger, indirect industrial discharger, municipal discharger). However, in each case a module

was used to automatically update the appropriate table. Appendix A contains the code used in the

modules.

2.10 DIRECT INDUSTRIAL DISCHARGERS

Data for the 36 direct discharge facilities were inserted in the industrial select tables

without any modification.

2.11 INDIRECT INDUSTRIAL DISCHARGERS

The original version of NWPCAM did not include records for indirect dischargers,

because their loadings were captured through the corresponding municipality. For this analysis,

flow rates from the meat-processing facilities and municipalities were separated. This approach

permitted adjustment of the loadings from the meat processing facilities without affecting the

municipalities. New records were inserted into the industrial select tables for each indirect

discharger. Pollutant concentration data were then multiplied by a factor to account for the
2 - 17

treatment received prior to discharge (see Table 2-5). The fractions estimate the proportion of

pollutant retained based on level of municipal treatment. The module linked the indirect

discharger to its municipality through the NPDES number. The treatment level of the

municipality was used to determine the appropriate multiplication factors for updating the

industrial select tables.

Table 2-5: Fraction of Pollutant Retained as a Function of Treatment Level

Treatment Type Level Fraction Retained

BOD5 TSS FCB TKN TN TP

Primary 2 0.70 0.50 0.65 0.78 0.78 0.87

Advanced Primary 3 0.50 0.30 0.65 0.78 0.78 0.87

Secondary 4 0.08 0.08 0.005 0.55 0.61 0.42

Advanced Treatment I 5 0.03 0.03 0.005 0.43 0.61 0.06

Advanced Treatment II 6 0.02 0.02 0.0000032 0.12 0.48 0.06

Default 9 0.08 0.08 0.005 0.55 0.61 0.42

Notes: BOD5

 = Five-day biochemical oxygen demand

TSS = Total suspended solids

FCB = Fecal coliform bacteria

TKN = Total Kjeldahl nitrogen

TN = Total nitrogen

TP = Total phosphorous

2.12 POTWs

Mass and flow balances were developed to calculate new effluent information for the

municipal facilities (see Equations 2 and 3). Appendix B contains full details on how the

equations were developed. 
2 - 18

(2)

(3)

where

Qmun,new = updated municipal flow rate (MGD)

Qmun,old = original municipal flow rate (MGD)

Qmeat = flow rate from the meat-processing facility (MGD)

Cmun,new = updated municipal concentration (mg/L)

Cmun,old = original municipal concentration (mg/L)

Cmeat = concentration in the meat-processing facility's effluent (mg/L)

fretained = fraction of pollutant retained after treatment.

Two municipalities received flow from multiple meat-processing facilities. For this

situation, equations were developed to calculate total flow and average concentrations to use for

Qmeat and Cmeat in Equations 2 and 3 (see Equations 4 and 5).

(4)

(5)

where

Qmeat = total flow rate from all meat-processing facilities (MGD)

Qmeat,1 = flow rate from meat-processing facility 1 (MGD)
2 - 19

Qmeat,2 = flow rate from meat-processing facility 2 (MGD)

Cmeat = average concentration in effluent from all meat-processing facilities

(mg/L)

Cmeat,1 = concentration in effluent from meat-processing facility 1 (mg/L)

Cmeat,2 = concentration in effluent from meat-processing facility 2 (mg/L).

There were nine meat-processing facilities that had a flow equal to or larger than their

corresponding municipalities. For these treatment plants, the flow rate was divided in half and

the original concentration values were retained.

Analysis using these equations revealed that there were 15 wastewater treatment plants

that had negative concentrations for one or more parameters. This occurred when the meat

facilities comprised a large fraction of the total municipal flow and/or when the meat facility

effluent concentration was much larger than the municipal concentration. Negative

concentrations were replaced by default concentration values based on treatment level (see Table

2-6).
2 - 20

Table 2-6: Default Effluent Characteristics by Treatment Level

Treatment Type Level Effluent Characteristics

BOD5 TSS FCB TKN TN TP

Primary 2 143.5 107.5 2.06E+06 23.4 23.4 5.2

Advanced Primary 3 102.5 64.5 2.06E+06 23.4 23.4 5.2

Secondary 4 16.4 17.2 1.58E+03 16.5 18.3 2.5

Advanced Treatment I 5 6.2 6.5 1.58E+03 12.9 18.4 0.4

Advanced Treatment II 6 4.1 4.3 1.00E+01 3.6 14.4 0.4

Default 9 16.4 17.2 1.58E+03 16.5 18.3 2.5

Notes: BOD5 = Five-day biochemical oxygen demand (mg/L)

TSS = Total suspended solids (mg/L)

FCB = Fecal coliform bacteria (MPN/100 mL)

TKN = Total Kjeldahl nitrogen (mg/L)

TN = Total nitrogen (mg/L)

TP = Total phosphorous (mg/L)
3 - 1

3.0 DATA SOURCES

EPA uses readily available Agency and other databases, models, and reports to evaluate

water quality effects. For the Meat and Poultry Processing (MPP) Environmental Assessment,

EPA used two basic sets of data. The first data set was used to develop baseline conditions

(current use levels) for stream reaches affected by MPP discharges. The second set of data were

used to develop estimates of individual facility pollutant loadings. These data sets were then

entered into the National Water Pollution Control Assessment Model (NWPCAM) to quantify

impacts of the MPP dischargers under current and regulatory treatment levels. The following

sections describe the specific types of data used to run the NWPCAM model and the primary

sources for those data.

3.1 POINT SOURCE LOADS USED IN NWPCAM TO ESTIMATE BASELINE

WATER QUALITY CONDITIONS

Point sources represented in NWPCAM 1.1 include municipal and industrial wastewater

treatment plants and combined sewer overflows. Pollutant discharges from municipal and

industrial outfall pipes are represented in the model by estimates of annual mean loading rates

input at a discrete location along the length of a stream or river. Pollutant discharges from urban

runoff and combined sewer overflows, accounted for by an urban network of multiple discrete

outfall pipes discharging to one or more waterways, are aggregated and distributed uniformly to

RF1 reaches within the urban land use portions of a watershed. Pollutant loads for point sources

are estimated for each of the following state variables selected for NWPCAM 1.1:

• 5-day biochemical oxygen demand (BOD5)

• Total Kjeldahl nitrogen (TKN)

• Dissolved oxygen (DO)

• Total suspended solids (TSS)
3 - 2

• Fecal coliform bacteria (FCB).

3.1.1 Municipal and Industrial Dischargers

3.1.1.1 Primary Data Sources.

The primary data sources used to estimate the magnitude and location of municipal and

industrial point source loads are the following EPA national databases:

• Permit Compliance System (PCS)

• Clean Water Needs Survey (CWNS)

• Industrial Facilities Database (IFD).

The PCS database, used by EPA to track compliance by a discharger with NPDES permit

limits, provides monthly or quarterly summaries of monitored effluent flow and concentration

data submitted to EPA as Discharge Monitoring Reports (DMRs) by "major" municipal and

industrial facilities. Standard Industrial Classification (SIC) codes are used to identify the type of

discharger (e.g., municipal, pulp and paper, allied chemicals). Data are generally not available in

PCS for numerous small facilities classified by EPA as "minor" based on criteria that include

effluent flow (<1 MGD), population served (<10,000), or a qualitative judgment of minimal

"water quality impact."

The CWNS provides an inventory of the existing and projected status of both major and

minor municipal wastewater treatment plants. The database contains records of population

served, effluent flow rates, influent and effluent concentrations, and loads of conventional

pollutants. The CWNS also includes a coded description to identify the category of each

treatment plant by the level of existing and projected wastewater treatment. The levels of

treatment performed by plants represented in the CWNS include the following treatment

technologies that are summarized briefly below:

• Raw (no treatment): Wastewater is collected and discharged to surface waters
3 - 3

without any removal of pollutants.

• Primary: Screens and physical settling of wastewater results in separation and

removal of heavy solids. Pollutants associated with large particles are removed.

• Advanced Primary: Enhanced settling and physical removal of pollutants are

achieved with low to high doses of chemical coagulants such as metal salts or

organic polyelectrolytes.

• Secondary: Removal of heavy solids by physical settling is followed by biological

processes designed to enhance bacterial growth to decompose organic materials.

• Biological treatment processes used are designed to enhance the growth of

suspended or attached bacteria in (a) activated sludge and waste stabilization

ponds and (b) trickling filters.

• Advanced Secondary: Physical settling and conventional biological treatment are

enhanced with either chemical coagulation or additional biological processes to

increase the removal efficiency of solids, BOD, and nutrients.

• Tertiary or Advanced Treatment: Physical settling and conventional biological

treatment are enhanced for very high removal efficiency with high dosage

chemical coagulation, biological processes for nitrification and denitrification,

filtration, and adsorption with granular activated carbon or reverse osmosis.

• No Discharge (to surface waters).

Technical details about these levels of municipal treatment can be obtained from standard

environmental engineering texts (e.g., Metcalf and Eddy, 1991).

The IFD provides comprehensive records on effluent discharges from the nation's major

and minor industrial facilities. A significant shortcoming of the IFD, however, is that EPA no

longer maintains this database. Therefore, these data are no longer completely up to date. The

RF1 database was used to link the locations of point source inputs with specific river-mile points

on an RF1 reach for input to the model.
3 - 4

3.1.1.2 Typical Pollutant Concentrations.

For many major municipal facilities, reliable estimates of effluent flow, BOD5, and TSS

concentrations were available from PCS and CWNS. Considerably fewer data were available to

characterize municipal effluent concentration levels of TKN and FCB. For estimates of DO

loads, effluent data were typically not reported by wastewater treatment facilities. Literature data

were used to assign effluent DO levels assuming 50 percent saturation (at 25 degrees C).

Thousands of industrial facilities are included in EPA inventories of the nation's

industrial wastewater dischargers. In both PCS and IFD, municipal and industrial facilities are

identified by their NPDES identification number and a Standard Industry Category (SIC) code.

For example, municipal sewage treatment facilities are assigned the SIC code of 4952. In general,

the availability of data to characterize effluent flow and pollutant loading rates for industrial

dischargers was more limited than for municipal facilities. Most of the largest industrial "major"

sources are, however, included.

For municipal and industrial point sources (major and minor) in which actual discharge

data were available from either PCS, IFD, or CWNS, those data were used to assign a loading

rate for input to the model. For municipal point sources for which effluent data were not

available, default effluent flow, loads, and concentrations, compiled from PCS, CWNS, and

other sources (e.g., Metcalf and Eddy, 1991; EPA, 1995; NRC, 1993), were used to estimate

typical pollutant loading rates for input to the model. For industrial point sources for which

effluent flow and pollutant loading data were not available, typical pollutant loads (TPLs) and

typical pollutant concentrations (TPCs), compiled as look-up tables for groups of four-digit level

SIC codes were obtained from the National Oceanic and Atmospheric Administration (NOAA,

1994) to develop NWPCAM 1.1.

3.1.1.3 Inventory of Point Source Facilities.

There were 8,878 reach-indexed municipal facilities and 23,118 reach-indexed industrial

facilities (direct discharge) included in NWPCAM version 1.1.
3 - 5

3.1.2 Urban Runoff and Combined Sewer Overflows

3.1.2.1 Primary Data Sources.

 The public works infrastructure in every town and city includes an urban stormwater

drainage system designed to collect and convey runoff from rainstorms and snow melt.

Stormwater runoff can contribute significant intermittent loading of pollutants with adverse

impacts on water quality and aquatic resources. EPA's National Urban Runoff Project (NURP)

concluded that wet weather events contribute significant loadings of pathogens, heavy metals,

toxic chemicals, and sediments (EPA, 1983). Over the past several years, EPA has worked

closely with state and local governments to design and implement effective programs to reduce

pollutant loading from urban runoff. Under the 1987 Amendments to the CWA, EPA published

regulations for general permits for stormwater discharges from urban areas (Phase 1, >100,000

population; Phase 2, <100,000 population) and industrial sites. Reduction of pollutant loads to

surface waters is typically accomplished using best management practices (BMPs) designed to

remove debris accumulation on paved surfaces and to attenuate the rate of urban stormwater flow

(Novotny and Olem, 1994).

As a vestige of public works practices in vogue from the nineteenth century (ca.

1850-1900), many older cities, primarily in the Northeast, Midwest, and Upper Midwest, have

urban drainage systems that were designed, for cost-saving reasons, to convey both stormwater

runoff and raw sewage. These combined sewer overflow (CSO) systems were intentionally

designed to overflow and discharge the mixture of raw sewage and stormwater into the nearest

urban waterway, when runoff from heavy rainstorms exceeded the hydraulic capacity of the

combined sewer pipe network. Although pollutant loading from CSOs occurring only during

heavy rainstorms is intermittent, high loading rates of pathogens often result in closure of

recreational beaches and shellfish beds to protect public health (Brosnan and Heckler, 1996).

Discharges from CSOs also can result in high loading rates of organic materials and

accumulations of noxious sludge beds near CSO outfalls with locally depressed levels of

dissolved oxygen. EPA 1997) estimates that about 880 older cities, including Washington, DC,

for example, still have combined sewer systems that periodically discharge a mixture of raw
3 - 6

sewage and stormwater runoff into urban waterways. Several cities have also initiated costly

construction projects to eliminate combined sewer systems by separating urban stormwater

drainage from raw sewage collection systems. In Minneapolis-St. Paul, MN, for example, an

aggressive $320 million (1996 dollars) construction program implemented over a 10-year period

from 1985-1995 eliminated the old combined sewer system and greatly improved compliance

with water quality standards for FCB levels in the Upper Mississippi River (MCES, 1996).

3.1.2.2 Typical Pollutant Concentrations

Based on data archived in EPA's NURP database (EPA, 1983) and data compiled by

Novotny and Olem (1994), a range of characteristic effluent concentrations is presented in Table

3-1 for urban runoff and for CSOs. The data in Table 3-1 illustrate the relative magnitude of the

range of characteristic effluent levels for urban runoff and CSOs. The urban runoff loading rates

used in NWPCAM 1.1 are based on data obtained from Lovejoy (1989) and Lovejoy and

Dunkelberg (1990).

Table 3-1. Effluent Characteristics of Urban Runoff and CSOs 

Parameter Urban Runoff CSO (Event Mean)

BOD5 (mg/L) 10-13 60-200 (115)

CBODU:CBOD5 3.0 1.4

TSS (mg/L) 141-224 100-1100 (370)

TKN (mg/L) 1.68-2.12 ND (6.5)

NH3-N (mg-N/L) ND ND (1.9)

NO2-N + NO3-N (mg-N/L) 0.76-0.96 ND (1.0)

Total N (mg-N/L) 3-10 3-24 (7.5)

Total P (mg-P/L) 0.37-0.47 1-11

Total lead (mg/L) 161-204 ND (370)

Total coliforms (MPN/100 mL) 103-108 105-107 (ND)

Note: ND = no data.

MPN = most probable number.
3 - 7

3.1.2.3 Primary Data Sources for Urban Runoff Estimates 

Annual urban runoff pollutant loading data have been compiled on a county-level basis

by Lovejoy (1989) and Lovejoy and Dunkelberg (1990). Urban runoff loads were first

transformed from county-level loads to catalog unit loads using the areal proportion of a county

in a given catalog unit. Urban and rural runoff loads were then allocated to RF1 stream reaches

based on the length of the reach and whether or not a populated place (1990 Census) was

allocated with the reach. Estimates of effluent loads derived from CSO inputs are based on an

analysis performed to support EPA's 1992 Clean Water Needs Survey (Tetra Tech, 1993) with

the inventory of CSO facilities reduced from 1300 to 880 by EPA (1997). Effluent loads from

CSOs are based on a pulse load driven by storm runoff volume and the pollutant load associated

with a 5-year, 6-hour duration design storm event. Using the design storm parameter values,

runoff volume was estimated from the CSO system drainage system, population served, and

degree of imperviousness. Table 3-2 presents a nationally aggregated summary of the loading

estimates used in NWPCAM 1.1 to represent pollutant loads contributed by urban runoff and

CSOs.

Table 3-2. National Summary of Annual Load Estimates for Urban and Rural Runoff

and CSOs (as metric tons/day)

Parameter Urban Runoff Rural Runoff CSOs

BOD5 1,701 19,974 2,823

TSS 3,081 778,638 10,361

3.2 NONPOINT SOURCE LOADS

Nonpoint source loads, characterized as intermittent diffuse inputs distributed over an

entire drainage basin, are related to hydrologic conditions, topography, physiography, and land
3 - 8

uses of a watershed. In NWPCAM 1.1, the county land-use data used by Lovejoy (1989) and

Lovejoy and Dunkelberg (1990) to estimate pollutant loads over a drainage basin were classified

very simply as either urban or rural. In NWPCAM 1.1, urban and rural runoff are the only

nonpoint sources of pollutant loads included in the model framework. The very broad category of

rural land uses accounts for essentially all other land uses not classified as urban (e.g., forest,

agricultural pasture, and crops). The data obtained from Lovejoy's work (1989) do not allow a

breakdown of rural nonpoint source loads into more detailed classifications of either forest or the

several subclassifications of agricultural land uses (e.g., grassland, pasture, feedlots, cropland).

To assign the catalog unit-based rural nonpoint source loads as an input load for each RF1

reach, the loads were attenuated using drainage area-dependent sediment delivery ratios (SDR)

assigned to each catalog unit (Vanoni, 1975). Since the data used to quantify rural nonpoint

source loads are so highly aggregated, evaluations of policy scenarios for BMP controls of

nonpoint sources are not possible in NWPCAM 1.1. Nonpoint source loads from rural land uses

are included in the model framework to account for the contribution and impact of nonpoint

source loads on water quality. The NPS data provided by Lovejoy and Dunkelberg is based on

work done by Giannessi at Resources For the Future. The urban loadings are estimated using a

simplified procedure that takes estimates of the number of urban residents per county and

multiplies that number by coefficients to get total loadings of urban pollutants to the water. The

"Lovejoy" rural loadings is a much more involved process that includes three modules. The first

module estimates sheet and rill erosion, first estimating the total tons of erosion, then applying

soil texture and stream density factors, which provides a net amount deposited to surface waters

by county. Then, the elemental composition of the surface soil and non-sheet and rill sources are

added in to get a total pounds of pollutants (BOD5, TSS, TKN, TP, Cu, Pb, Fe, Zn) reaching the

surface waters. The second module accounts for livestock runoff by estimating the total manure

generated by county, then applying USDA estimates of manure "losses" by state from

volatilization, runoff, and seepage. The total quantity "lost" to surface waters is then partitioned

into physical/chemical characteristics by assuming 12 percent of the total manure is total solids

(TS), BOD5 is 23 percent of TS, COD is 95 percent of TS, TKN is 4.9 percent of TS, and TP is
3 - 9

1.6 percent of TS. This calculation produces total annual estimates of these pollutants reaching

surface waters by county. The third module is a nutrient runoff module using the Cornell Nutrient

Simulation Model; outputs from this module are not used in NWPCAM 1.1.

3.3 FACILITY-SPECIFIC LOADING DATA

EPA used various sources for collecting data on MPP facilities. The Agency obtained

data through EPA site visits and sampling, and facility responses to 2001 Meat Products Industry

Survey (herein referred to as the "Detailed Survey"). Information from the Detailed Survey

provided many of the facility-specific parameters required for this analysis, such as annual

discharge volume, current pollutant loadings, and discharge location information (i.e., name of

receiving water body). EPA's data collection procedure is described in detail in Chapter 3 of the

technical development document. 

For the MPP facilities which responded to the Detailed Survey, EPA identified discharge

location based primarily on NPDES information provided in the Survey. For indirect dischargers,

EPA also used NPDES information provided by the respondent. Where such information was not

available, EPA contacted the facility or performed additional analysis using either the EPA's

Permit Compliance System (PCS) or the Industrial Facilities Discharge (IFD) database to identify

the appropriate POTW.

EPA also extracted facility-specific pollutant loading information from the Detailed

Surveys. Facilities respondents provided final discharge information for a suite of pollutants. As

noted above and described in Chapter 2 of this document, the NWPCAM model assessed four

conventional pollutants: DO, BOD, TSS, and FCB. In addition, loadings for TKN were also

included to support the modeling of DO and BOD.
3 - 10
4 - 1

4.0 RESULTS

This section presents EPA's estimate of the water quality effects of Meat and Poultry

Processing (MPP) discharges under baseline conditions and following the adoption of the

proposed limits and standards. In addition, analytical results are presented for regulatory options

that were evaluated by EPA, but not included in today's proposal.

EPA used the National Water Pollution Control Assessment Model (NWPCAM) to

estimated the potential benefits of controlling discharges of bio-chemical oxygen demand

(BOD5), total suspended solids (TSS), total Kjeldahl nitrogen (TKN) and fecal coliform bacteria

(FCB) from MPP facilities. A total of 97 MPP facilities were modeled for this analysis, including

36 direct and 61 indirect dischargers. EPA estimates that 246 direct and 731 indirect discharges

are within scope of the regulatory options evaluated for this proposed rule.

The first subsection (4.1) presents a summary of the overall results, modeled treatment

options, modeled facilities, environmental scale-up factor and limitations of the Water Quality

Environmental Assessment (WQEA). The second subsection, 4.2., presents documented impacts.

4.1 WQEA RESULTS SUMMARY

EPA modeled a sample set of 97 MPP facilities with a combined baseline loading of 49.9

million lb/yr (see Table 4-1). The baseline loadings consist of the following pollutants: BOD,

TSS, nitrogen, phosphorus and TKN. 

EPA estimates the preferred treatment option (Scenario 7) for this proposed rule would

reduce pollutant discharges from 36 MPP facilities by 4.8 million lb/yr. For this 10 percent

reduction, EPA estimates that this proposed rule would improve the WQI of 949 reach miles

(6,687 miles for the national set). The average WQI for these 949 reaches increases from 74.9 to
4 - 2

75.9 (see Table 4-1) which is still just below the boatable criteria breakpoint of WQI = 79. The

standard deviation suggest that 67 percent of the reaches impacted are located in the WQI range

of 60 - 92. 

Table 4-1: Benefits Scenarios Modeled (97 facilities)1

Scenar io 2 Regulatory

Options 3

Pollutant 4

Load

(million

lbs/yr)

Pollutant

Reduction

(percent)

Step 5

 Improvement:

Overall use

(reach miles)

Contiuou s 6

Improvement:

Water Quality Index

(reach miles)

Sample 7 National 8 Sample National

Baseline 49.9

1 BAT2 47.5 5 17 116 926 6,325

2 BAT3 45.0 10 21 143 949 6,482

3 BAT4 44.8 10 21 143 952 6,502

4 BAT2 + PSES1 36.2 27 24 200 1216 9,799

5 BAT3 + PSES1 33.7 32 28 227 1240 9,968

6 BAT4 + PSES1 33.5 33 28 227 1244 10,000

7 BAT 3 (meat,

poultry), BAT2

(Rendering)

45.1 10 21 143 949 6,687

8 BAT 3 (meat,

poultry), BAT2

(Rendering) +

PSES1

33.7 32 28 227 1240 9,813

1 This table corrects several errors reported in preamble Table IX.G-1. For more information, please see Appendix A.

2 EPA is proposing Scenario 7 for the MPP effluent guideline rule making

3 BAT = Best Available Treatment (for Direct Discharges)

PSES= Pretreatment Standards for Existing Sources (for Indirect Dischargers)

4 Pound totals include BOD, TSS, Nitrogen, Phosphorus and TKN. Some overlap between categories may be occurring

5

Improvement credited when threshold conditions are met.

6

Improvement credited for any measurable change in water quality.

7 Sample set represents 97 facilities (36 direct and 61 indirect). 

8 National set represents 977 facilities (246 direct and 731 indirect).

In addition to estimating the continuous change in water quality, EPA also analyzed the
4 - 3

use category or step change approach. The reductions in loadings from the Scenario 7 with this

approach would result in the improved overall use of 21 reach miles (for the sample set), which

scales up to 143 reach miles (for the national set).

The large differences in miles of stream reaches affected attributable to these two

approaches is intuitively consistent: the continuous approach will count all stream reaches where

decreases in pollutants occur, whereas the use category approach will only estimate those

instances where a change in water quality results in migrating from one use category to another. 

The continual approach is considered to be the preferred method of estimating water quality

impacts. The Economic Assessment presents the monetized benefits for this proposed rule,

which are based on the continuous approach.

Table 4-2: Water Quality Index (WQI) Baseline and Proposed Treatment Level Statistics

Scenario Average

WQI 1

Standard

Deviation

(SD)

Mean + SD 

Range 2

Min. Max. 3

Baseline 74.9 16 59 - 91 6 99

BAT3 (meat, poultry),

BAT2 (Re ndering)

75.9 16.3 60 - 92 6 99

1 Boatable criteria is 79. Reaches with WQIs less than 79, are designated as supporting no use.

2 Represent the interval by which 67% of reaches are represented

Fishable criteria is 94.4.

3

Swimmable criteria is 99.

4.1.1 Treatment Options Modeled

EPA modeled four treatment options for analysis (see Table 4-3). Three of the treatment

options are for facilities which discharge directly to a water body (i.e., direct dischargers). EPA

designates the treatment options for direct dischargers as best available technology (BAT). One

of the treatment options is for facilities which discharge indirectly to a water body (i.e., indirect

dischargers), through a publicly owned treatment work (POTW). EPA designates the treatment
4 - 4

options for existing indirect dischargers as pretreatment standards for existing sources (PSES).

The combination of BAT and PSES scenarios modeled is presented in Table 4-1. 

Table 4-3: MPP Regulatory Treatment Options

Regulatory Option 1 Technical Component

BAT2 Dissolved Air Flotation (DAF) (advanced oil/water separation),

Lagoon, and Disinfection (Oil and Grease, BOD5, TSS, Pathogen

removal) + Nitrification (NH3 removal)

BAT3 BAT2 + Denitrification (Nitrate removal)

BAT4 BAT3 + (Phosphorus removal)

PSES1 DAF, Equalization (Oil and Grease, TSS, removal)

1 BAT = Best Available Treatment (covers existing Direct Dischargers)

PSES = Pretreatment Standards for Existing Sources (covers existing Indirect Dischargers)

4.1.2 Facilities Modeled

EPA had sufficient data to model 97 out of the 977 meat, poultry, and rendering facilities

which are in scope of the regulatory options evaluated in this proposed rule. To prepare for the

Water Quality Environmental Assessment (WQEA) and a separate economic analysis, EPA

mailed out 350 detailed surveys to generate both environmental and economic data. EPA

received 241 detailed surveys in time for data analysis of this proposed rule making. Of the 241

detailed surveys, EPA received sufficient data to model the environmental impacts of 97

facilities (36 direct dischargers and 61 indirect dischargers). EPA did not evaluate 79 facilities

with zero discharges or 65 facilities for which EPA had insufficient data to conduct the water

quality analysis.

4.1.3 Simplified Environmental Scale-up Factor

EPA developed environmental scale-up factors for both the direct and indirect facilities.
4 - 5

The environmental scale-up factors are ratios between the number of facilities in scope and

modeled. These scale-up factors allow EPA to approximate what the environmental impact of the

proposed rule might be on the national level. These weighting factor were only used for

estimating water quality impacts. EPA presents the separate methodology used for scaling of the

monetized benefits in the Economic Analysis.

EPA estimates that 246 direct discharger facilities are in scope of the evaluated BAT

options. Since EPA modeled 36 direct dischargers, the ratio of in scope directs (246) to modeled

directs (36), is 6.83, or

246 (in scope direct dischargers)

Scaling Factor direct dischargers = -------------------------------

36 (modeled direct dischargers)

= 6.83

EPA estimates that 731 indirect discharger facilities are in scope of the evaluated PSES1

option. Since EPA modeled 61 indirect dischargers, the ratio of in scope indirects (731) to

modeled indirects (61) is 11.98, or

731 (in scope indirect dischargers)

Scaling Factor indirect dischargers = --------------------------------

61 (modeled indirect dischargers)

 = 11.98

4.1.4 Limitations of the WQEA

EPA believes that its analysis likely underestimates the potential benefits of the

regulatory options evaluated for this proposal. Specifically, the current version of the NWPCAM
4 - 6

model used for this environmental assessment only models DO, BOD, fecal coliform bacteria,

TKN and TSS. (See Chapter 2.) Accordingly, the analysis presented in today's proposal addresses

only a subset of MPP effluent contaminants. EPA intends to modify the model in support of the

final rule to include the following: (1) modeling of nutrients for an eutrophication analysis of

ponds and lakes; and 2) modeling of other pollutants for rivers and streams. 

EPA did not evaluate the human health benefits associated with reduction of toxic

pollutant discharges, because MPP effluents do not contain significant levels of toxic

contaminants. Nonetheless, it is possible that MPP pollutants, especially nitrates could have an

impact on certain human receptors, if contaminants reach drinking water supplies.

EPA also did not evaluate the effects of MPP discharges on POTWs. MPP facilities discharge

mostly conventional pollutants (BOD5, TSS, oil and grease, and fecal coliform bacteria), which

POTWs are designed to treat.

4.2 DOCUMENTED ENVIRONMENTAL IMPACTS AND PERMIT VIOLATIONS

In addition to modeling environmental effects of MPP facilities using the NWPCAM

model, EPA performed a literature search to document cases where meat and poultry processing

facilities have been identified as sources of water quality impairment. The results of this

literature search are published in the Administrative Record as part of the public docket.

While the literature search was not comprehensive and was limited mostly to newspaper

articles and government press releases covering the last five years, EPA found 20 cases in which

plant operators were cited for for a variety of permit violations. One meat processing facility was

cited for more than 5,000 permit violations, which led to degradation of water quality in the

affected river. In fact, this facility received the highest fine ever issued under the Clean Water

Act. Other documented impacts cited in the articles included ten stream reaches with nutrient

loadings, two sites with contaminated well water, one site with contaminated ground water, and
4 - 7

one lake threatened by nutrient loadings. In all cases, the identified source of contamination or

perceived threat is an MPP facility. In cases in which permit levels were violated or alleged to be

violated, NH3-N, PO4 , fecal coliform bacteria, and TSS were the most common contaminants of

concern.

Eighteen of the articles document legal action in criminal cases taken against meat and

poultry processing facility owners or operators . Documented legal action includes: (1)

conspiracy of five facilities to violate the CWA; (2) one case of illegal dumping of waste; and (3)

five cases of falsifying records, diluting waste samples, and/or destroying records. These legal

actions resulted in possible cases of incarceration and fines ranging from $0.25 million to $12.6

million. Table 4-4 summarizes the environmental impacts identified and type of legal action

pursued.
4 - 8

Table 4-4. Documented Environmental Effects of MPP Wastes on Water Quality

Identified Impacts

Case #1 High concentrations of fecal coliform, an indicator of the presence of animal intestinalwaste found in receiving waters. 

Also excessive discharges of phosphorus, ammonia, cyanide, oil, and grease. Plant was fined $12.6 million , the

largest Clean Water Act fine ever (1997). 

Case #2 Operators of five poultry processing facilities were indicted for actions leading to more than 5,000 permit violations

during a 20-year period from 1975-1995.. Indictment (01/2000) alleged one of the plants pined pollutants in the form

of ammonia nitorgen, fecal coliform, oil and grease, suspended solids, and other rotting materials directly into

receiving waters. 

Case #3 Poultry processing plant agreed to pay $500,000 (1998) for permit violations. Parameters on the discharge of

phosphorus were also established for the first time for this facility. 

Case #4 Meat processing facility operators agreed to pay fine of $250,000 for permit violations. Permit violations included

falsification of discharge monitoring reports, exceedances of effluent limitations, and inadequate record-keeping

practices (1998) 

Case #5 Turkey processor agreed to make improvements is wastewater treatment system and pay $300,000 fine for permit

violations. Violations included exceeding limitations fro phosphorus and ammonia (1997). High levels of these

pollutants were found downstream from plant. Biologists also found a dearth of aquatic insects.

Case #6 Rendering facility officials agreed to pay $600,000 in fines for polluting river with dead animal parts and falsifying

sewer discharge records (2000).

Case #7 Chicken processing plant was fined $10,800 for permit violations. Wastewater exceeded limits on fecal coliform and

also exceeded volume limits. During 1998, a fish kill caused by oxygen depleted water was tied to facility’s treatment

plant.

Case #8 Two poultry plants were fined more than $46,000 for 206 water quality violations that took place during 1998 and

1999. Waste with high bacteria levels was running off sprayed fields. 
4 - 9

Table 4-4. Documented Environmental Effects of MPP Wastes on Water Quality (continued)

Identified Impacts

Case #9 A poultry plant was fined $6 million for allowing excessive runoff from its farms and processing plants. 

Case #10 Pork Processing plant cited 20 times since 1994 for permit violations. Tests of receiving water body indicated high

levels of several pollutants including ammonia and fecal bacteria.

Case #11 High levels of phosphorous were detected downstream from poultry processing plant. In addition, state alleged that

high levels of ammonia and high temperatures resulted from plant’s discharges.

Case#12 State Conservation Commission study indicated that waste from poultry processing plans threatened viability of lake

due to discharges of phosphorous and nitrogen.

Case#13 Water Quality data collected by EPA indicated marked increase in phosphorous in many areas downstream from

chicken plants.

Case#14 State Department of Natural Resources obtained a court order to compel poultry processor to adhere to State Water

Quality Laws. The plant will reduce its discharge by about 50 percent under the court order.

Case#15 State environmental official filed suit against poultry processor for willfully contaminating groundwater in the vicinity 

of fields where the plant had sprayed with wastewater. Wastewater was laden with nitrates (1998)

Case#16 Owner of meat slaughter house indicted for allegedly dumping blood and other animal waste products into nearby

water bodies (2000)

Case#17 State issued an order containing a $25,000 fine for violating permit limits for ammonia, solids, and other pollutants.

Case #18 Operator of rendering plant sentenced for one month in prison for illegally discharging pollutants int river (1998). 

Ammonia and other pollutants were discharged and monitoring reports falsfied.

Case #19 Meat further processing firm was fined $28,000 for railing to file proper forms for discharge of oil, grease, TSS, and

BOD (1998). Consent agreement also required company to install pollution equipment.

 
4 - 10
5 - 1

5.0 REFERENCES

Brosnan, T.M., and P.C. Heckler. 1996. “The Benefits of CSO Control: New York City

Implements Nine Minimum Controls in the Harbor.” Water Environment &

Techechnology 8(8): 75-79.

Carson, Richard T. and Robert Cameron Mitchell. 1993. The Value of Clean Water: The Public’s

Willingness to Pay for Boatable, Fishable, and Swimmable Quality Water. Water Resources

Research, 29(7 July):2445-2454.

Di Toro, D.M. 1986. “A Diagenetic Oxygen Equivalents Model of Sediment Oxygen Demand.” 

In Hatcher, K.J. (ed.). Sediment Oxygen Demand, Processes, Modeling and

Measurement. Institute of Natural Resources, University of Georgia, Athens, GA. pp.

171-208.

Leo, W.M, R.V. Thomann, and T.W. Gallagher. 1984. Before and After Case Studies:

Comparisons of Water Quality Following Municipal Treatment Plant Improvements.

EPA 430/9-007. Washington, DC: U.S. Environmental Protection Agency, Office of

Water, Program Operations.

Lovejoy, S.B. 1989. Changes in cropland loadings to surface waters: Interim report No. 1 for

the development of the SCS National Water Quality Model. Purdue University, West

Lafayette, Indiana.

Lovejoy, S.B., and B. Dunkelberg. 1990. Water quality and agricultural policies in the 1990s: 

Interim report No. 3 for development of the SCS National Water Quality Model. Purdue

University, West Lafayette, Indiana.

McClelland, Nina I. 1974. Water Quality Index Application in the Kansas River Basin. Prepared for U.

S. EPA-Region VII.

National Oceanic and Atmospheric Administration (NOAA). 1994. Gulf of Maine Point Source

Inventory, A Summary by Watershed for 1991. National Coastal Pollutant Discharge

Inventory. Pollution Sources Characterization Branch, Strategic Environmental

Assessments Division, National Oceanic Atmospheric Administration, Silver Spring,

MD, December. 

Novotny, V. and H. Olem. 1994. Water Quality Prevention, Identification and Management of

Diffuse Pollution. New York: Van Nostrand Reinhold. 

NRC. 1993. Managing Wastewater in Coastal Urban Areas. Committee on Wastewater

Management for Coastal Urban Areas, Water Science and Technology Board,
5 - 2

Commission on Engineering and Technical Systems, National Research Council,

National Academy Press, Washington, DC. 

Redfield, A.C., B.H. Ketchum, and F.A. Richards. 1963. “The Influence of Organisms on the

Composition of Seawater.” In The Sea, Vol.2, M.N. Hill (ed.). pp. 26-77. New York: 

Wiley-Interscience. 

U.S. EPA, 1983. Technical Guidance Manual for Performing Wasteload Allocations. Book

Two: Streams and Rivers. Chapter 2: Nutrient/Eutrophication Impacts. Office of Water

Regulations and Standards, Monitoring and Data Support Division.

U.S. EPA (Environmental Protection Agency). 1995. Technical Guidance Manual for

Developing Total Maximum Daily Loads, Book II: Streams and Rivers, Part 1:

Biochemical Oxygen Demand/Dissolved Oxygen and Nutrients/Eutrophication. EPA 823-

B-97-002. Office of Water, Washington, DC. September.

U.S. EPA. 2001. Environmental and Economic Benefit Analysis of Proposed Revisions to the National

Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated

Animal Feeding Operations, Chapter 4, Modeling of Improvements in Surface Water Quality and

Benefits of Achieving Recreational Use levels. Washington: EPA/Office of Water, EPA-821-R01-002. January, 2001.

Vaughan, William J. 1986. The RFF Water Quality Ladder, Appendix B in Robert Cameron Mitchell

and Richard T. Carson, The Use of Contingent Valuation Data for Benefit/Cost Analysis in

Water Pollution Control, Final Report. Washington:Resources for the Future.
A - 1

APPENDIX A: PREAMBLE CORRECTION 

Two corrections have been made since the signing of the MPP preamble. Corrections are

noted as follows: Black line strikeouts (shown as: black line strikouts) represent original text that

has been removed. Redline strikeouts (shown as: redlined text) represent newly added text.

The first correction is a typo found in the “Sample” column of Table IX.G-1 (page 155). 

The value of 21, should actually be 28.

Table IX.G-1: Modeled Environmental Benefits (97 facilities)

Scenario Regulatory Options Pollutant 1

 Load

(million lbs/yr)

Pollutant

Reduction

(percent)

Overall use

improvement 2

(reach miles)

Sample National

Baseline 49.9

1 BAT2 47.5 5% 17 29 116

2 BAT3 45.0 10% 21 36 143

3 BAT4 44.8 10% 21 36 143

4 BAT2 + PSES1 36.2 27% 24 41 200

5 BAT3 + PSES1 33.7 32% 28 48 227

6 BAT4 + PSES1 33.5 33% 21 28 36 227

7 BAT3 (meat, poultry),

BAT2 (Rendering)

45.1 10% 21 36 143

8 BAT3 (meat, poultry),

BAT2 (Rendering) +

PSES1

33.7 32% 28 48 227

Note 1: Baseline = 49.9 Million lbs/yr. Pound totals include BOD, TSS, Nitrogen, Phosphorus and TKN

from 97 facilities. Some overlap between categories may be occurring

Note 2: Sample set represents 97 facilities (36 direct and 61 indirect). National set represents 246 direct

and 731 indirect discharger facilities. Of the 246 facilities represented, 79 facilities are zero

dischargers, and therefore do not contribute to these modeled water quality impacts /

improvements.
A - 2

The second correction has to do with the scale-up of the overall use improvement to the

national level. EPA originally used a scale-up factor of 1.72 which incorrectly assumed that the

246 facilities covered by this rule, consisted of both direct, indirect and land applying (or zero

discharger) facilities. The calculation was done as follows:

Scaling Factor = 246 (Facilities in scope) - 79 (zero dischargers)

97 (Modeled Facilites)

= 1.72

The 246 facilities are actually the direct dischargers in scope of this rule. Therefore a

scale-up factor for the direct dischargers based on a simple ratio of the total number of directs

(246) to the number of directs modeled (36) is 6.83, or

246 (in scope direct dischargers)

Scaling Factor direct dischargers

 = -------------------------------

36 (modeled direct dischargers)

 = 6.83

An example calculation of scaling BAT3 overall use improvement to the national level is

as follows:

Use Improvement BAT3 national = BAT3 sample x Scaling Factor direct dischargers

= 21 mi. x 6.83

= 143 mi

EPA estimates that 731 indirect facilities are in-scope of the PSES1 option. Therefore,
A - 3

the scale-up factor for indirect dischargers based on a simple ratio of the total number of

indirects (731) to the number indirects modeled (61) is 11.98, or 

731 (in scope indirect dischargers)

Scaling Factor indirect dischargers

 = --------------------------------

61 (modeled indirect dischargers)

 = 11.98

An example calculation of scaling PSES1 overall use improvement to the national level is

as follows: 

PSES1 Use Improvement sample = (BAT3+PSES1)sample - BAT3sample

= 28 mi - 21 mi

= 7 mi

PSES1 Use Improvement national = PSES1 sample x Scaling Factor indirect dischargers

= 7 mi x 11.98

= 84 mi

An example calculation of the scale-up of BAT3 + PSES1 overall use improvement to the

national level is as follows:

(BAT3 + PSES1) national = BAT3 national + PSES1 national

= 143 mi + 84 mi

= 227 mi
A - 4

As a result of this correction to the scaling methodology, EPA updated preamble Table

IX.G-1 (see table above). EPA also corrects the following two sentences found in the preamble:

(page 150): “ EPA estimates the national improvement in overall use to be 29 116 to 49 227

reach miles. 

(page 154): “The recommended treatment option would result in the over-all use improvement

of 21 river miles at the sample set, and approximately 36 143 miles at the national

level.”
B - 1

Appendix B: Equations used to update municipal facility loadings

Variable Definition

Qmun,new = updated municipal flow rate (MGD)

Qmun,old = original municipal flow rate (MGD)

Qmeat = flow rate from the meat-processing facility (MGD)

Qo = flow rate to municipality from other (non-meat) sources (MGD)

Cmun,new = updated municipal concentration (mg/L)

Cmun,old = original municipal concentration (mg/L)

Cmeat = concentration in the meat-processing facility’s effluent (mg/L)

Co = concentration in the flow from other (non-meat) sources (mg/L)

f = fraction of pollutant retained after treatment

Flow Balance

Qmun,old = Qmun,new + Qmeat

Therefore

Qmun,new = Qmun,old - Qmeat

Mass Balance

Mass in – Mass out – Mass depleted = 0 (Steady-state)
B - 2
C - 1

Appendix C: Modules

Module 1: Update direct industrial dischargers

Sub UpdateTT Ind()

'Create 9/14/01 by Megan Tulloch

'Last modified 9/20/01 by Megan Tulloch

'Modified 12/4/01 by A. Miles

'Tables

Dim dbs As D atabase

Dim ttindselect As Recordset

Set dbs = CurrentDb()

Set ttindselect = dbs.OpenRecordset("TTDirect")

'Variables

Dim v_bod As V ariant

Dim v_tss As Variant

Dim v_tn As Variant

Dim v_tp As Variant

Dim v_fec As Variant

Dim v_tkn As Variant

Dim v_npdes As Variant

Dim v_flow As Variant

'SQL query statement variables

Dim UpdqryStr As String

Dim SelqryStr As String

'Open T T Industrial Data

SelqryStr = "select * from TTD irect where (o ption = 'BA T4' Or o ption = 'PSES1');"

Set ttindselect = dbs.OpenRecordset(SelqryStr)

DoCmd .SetWarnings Fa lse

ttindselect.MoveFirst

Do Until ttindselect.EOF

'Set variables to TT industrial data values

 v_bod = ttindselect!BOD

 v_tss = ttindselect!TSS

 v_tn = ttindselect!TN

 v_tp = ttindselect!TP

 v_fec = ttindselect!FEC

 v_tkn = ttindselect!TKN

 v_npdes = ttindselect!NPDES

 v_flow = ttindselect!Flow 'If TT flow rates are in gpd, need to add conversion factor!

'Update NWPCAM industrial select table with TT values by corresponding NPDES

UpdqryStr = "UPDATE indselect SET " & _

"flow = " & v_flow & ", bod = " & v_bod & ", tss= " & v_tss & ", tn= " & v_tn & ", tp= " & v_tp & ", fec=

" & v_fec & " , tkn= " & v_tkn & " " & _

"WH ERE n pdes = '" & v_npde s & "';"

DoCmd.RunSQ L (UpdqryStr)

ttindselect.MoveNext

Loop

ttindselect.Close

DoCmd.SetW arnings True

End Sub

Module 2: Insert Indirect Industrial Facilities
C - 2

Sub InsertTTMuntoInd()

'Create 9/14/01 by Megan Tulloch

'Last modified 9/20/01 by Megan Tulloch

'Modified 1/8/02 by A Miles

'Tables

Dim dbs As D atabase

Dim ttmunselect As Recordset

Dim modmunselect As Recordset

Set dbs = CurrentDb()

'Variables

Dim i As Long

Dim v_npdes As Variant

Dim v_type As Variant

Dim v_bod As V ariant

Dim v_tss As Variant

Dim v_tn As Variant

Dim v_tp As Variant

Dim v_fec As Variant

Dim v_tkn As Variant

Dim v_ttflow

Dim v_primary_BOD As Variant

Dim v_primary_TSS As V ariant

Dim v_primary_TN A s Variant

Dim v_primary_TP A s Variant

Dim v_primary_FEC As V ariant

Dim v_primary_TKN As Variant

Dim v_advprimary_BO D As Variant

Dim v_advprimary_TSS As Variant

Dim v_advprimary_TN As Variant

Dim v_advprimary_TP As Variant

Dim v_advprimary_FEC A s Variant

Dim v_advprimary_TK N As Variant

Dim v_secondary_BO D As Variant

Dim v_secondary_TSS As Variant

Dim v_secondary_TN As Variant

Dim v_secondary_TP As Variant

Dim v_secondary_FEC A s Variant

Dim v_secondary_TK N As Variant

Dim v_advwt1_BO D As Variant

Dim v_advwt1_TS S As Variant

Dim v_advwt1_TN As Variant

Dim v_advwt1_TP As Variant

Dim v_advwt1_FEC As Variant

Dim v_advwt1_TK N As Variant

Dim v_advwt2_BO D As Variant

Dim v_advwt2_TS S As Variant

Dim v_advwt2_TN As Variant

Dim v_advwt2_TP As Variant

Dim v_advwt2_FEC As Variant

Dim v_advwt2_TK N As Variant
C - 3

Dim v_default_BOD A s Variant

Dim v_default_TSS As Variant

Dim v_default_TN As V ariant

Dim v_default_TP As Variant

Dim v_default_FEC As Variant

Dim v_default_TKN As Variant

Dim v_seqno As Variant

Dim v_mi As Variant

Dim v_do As Variant

Dim v_cbodtoubod As Variant

Dim v_psfbod As Variant

Dim v_psftss As Variant

Dim v_cu As Variant

Dim v_seg As Variant

Dim v_name As Variant

'SQL Query Statement Variables

Dim TTSelqryStr As String

Dim SelqryStr As String

Dim UpdqryStr As String

Dim InsqryStr As String

 v_primar y_BO D = 0.7

 v_primar y_TSS = 0.5

 v_primary_TN = 0.78

 v_primary_TP = 0.87

 v_primary_FEC = 0.65

 v_primary_TKN = 0.78

 v_advp rimary_B OD = 0.5

 v_advp rimary_T SS = 0.3

 v_advprimary_TN = 0.78

 v_advprimary_TP = 0.87

 v_advprimary_FEC = 0.65

 v_advprimary_TKN = 0.78

 v_secondary_BOD = 0.08

 v_secondary_TSS = 0.08

 v_secondary_TN = 0.61

 v_secondary_TP = 0.42

 v_secondary_FEC = 0.0005

 v_secondary_TKN = 0.55

 v_advwt1_BOD = 0.03

 v_advwt1_TSS = 0.03

 v_advwt1_TN = 0.61

 v_advwt1_TP = 0.06

 v_advwt1_FEC = 0.0005

 v_advwt1_TKN = 0.43

 v_advwt2_BOD = 0.02

 v_advwt2_TSS = 0.02

 v_advwt2_TN = 0.48

 v_advwt2_TP = 0.06

 v_advwt2_FEC = 0.0000032

 v_advwt2_TKN = 0.12

 v_default_BOD = 0.08

 v_default_TSS = 0.08
C - 4

 v_default_TN = 0.61

 v_default_TP = 0.42

 v_default_FEC = 0.0005

 v_default_TKN = 0.55

'Open T T Mu nicipal Da ta

TTS elqryStr = " select * from T TIndire ct where op tion = 'BAC ';"

Set ttmunselect = dbs.OpenRecordset(TTSelqryStr)

DoCmd .SetWarnings Fa lse

i = 0

ttmunselect.MoveF irst

Do Until ttmunselect.EOF

i = i + 1

'Select Row in NWPCAM Model Data corresponding TT data by NPDES

SelqryStr = "select * from munselect wh ere npde s = '" & ttmuns elect!NP DES & "';"

Set modmunselect = dbs.OpenRecord set(SelqryStr)

'Set variable from TT data to be m oved into N WPCAM Industrial Table

 v_npdes = ttmunselect!NPDES

 v_type = "INDIRECT"

'Set Flow variables to be used in calculating loads

 v_ttflow = ttmunselect!Flow 'If TT flows are in gpd, need to add conversion factor.

If modmunselect!LEVEL = 2 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_primary_BOD

 v_tss = ttmunselect!TSS * v_primary_TSS

 v_tn = ttmunselect!TN * v_primary_TN

 v_tp = ttmunselect!TP * v_primary_TP

 v_fec = ttmunselect!FEC * v_primary_FEC

 v_tkn = ttmunselect!TKN * v_primary_TKN

ElseIf modmunselect!LEVEL = 3 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advprimary_BOD

 v_tss = ttmunselect!TSS * v_advprimary_TSS

 v_tn = ttmunselect!TN * v_advprimary_TN

 v_tp = ttmunselect!TP * v_advprimary_TP

 v_fec = ttmunselect!FEC * v_advprimary_FEC

 v_tkn = ttmunselect!TKN * v_advprimary_TKN

ElseIf modmunselect!LEVEL = 4 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_secondary_BOD

 v_tss = ttmunselect!TSS * v_secondary_TSS

 v_tn = ttmunselect!TN * v_secondary_TN

 v_tp = ttmunselect!TP * v_secondary_TP

 v_fec = ttmunselect!FEC * v_secondary_FEC

 v_tkn = ttmunselect!TKN * v_secondary_TKN

ElseIf modmunselect!LEVEL = 5 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advwt1_BOD

 v_tss = ttmunselect!TSS * v_advwt1_TSS

 v_tn = ttmunselect!TN * v_advwt1_TN

 v_tp = ttmunselect!TP * v_advwt1_TP

 v_fec = ttmunselect!FEC * v_advwt1_FEC

 v_tkn = ttmunselect!TKN * v_advwt1_TKN
C - 5

ElseIf modmunselect!LEVEL = 6 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advwt2_BOD

 v_tss = ttmunselect!TSS * v_advwt2_TSS

 v_tn = ttmunselect!TN * v_advwt2_TN

 v_tp = ttmunselect!TP * v_advwt2_TP

 v_fec = ttmunselect!FEC * v_advwt2_FEC

 v_tkn = ttmunselect!TKN * v_advwt2_TKN

ElseIf modmunselect!LEVEL = 9 Then

'Calculate con centrations fo r both TT and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_default_BOD

 v_tss = ttmunselect!TSS * v_default_TSS

 v_tn = ttmunselect!TN * v_default_TN

 v_tp = ttmunselect!TP * v_default_TP

 v_fec = ttmunselect!FEC * v_default_FEC

 v_tkn = ttmunselect!TKN * v_default_TKN

 End If

'Insert into the NWPCAM industrial select table new values

InsqryStr = "INSERT INTO indselect " & _

"(flow,bod,tss,tn,tp,fec,tkn,npdes,type)" & _

"VALUES (" & v_ttflow & "," & v_bod & ", " & v_tss & ", " & v_tn & ", " & v_tp & ", " & v_fec & ", " &

v_tkn & ", '" & v_npdes & "', '" & _

v_type & "');"

DoCmd.RunSQ L (InsqryStr)

'Set variables to be move d from NWPCAM Municip al Select table driectly into Ind ustrial Select T able

 v_seqno = modmunselect!seqno

 v_mi = modmunselect!MI

 v_do = modmunselect!DO

 v_cbodtoubod = modmunselect!CBODTOUBOD

 v_psfbod = modmunselect!PSFBOD

 v_psftss = modmunselect!PSFTSS

 v_cu = modmunselect!CU

 v_seg = modmunselect!SEG

 v_name = modmunselect!NAME

'Update the new row with th e constant d ata to cop ied from the NWPCAM municipal se lect table

UpdqryStr = "UPDATE indselect SET " & _

"name = '" & v_name & "', seqno = " & v_seqno & ", mi = " & v_mi & ", do= " & v_do & ", cbodtoubod=

" & v_cbodtoubod & ", psfbod= " & v_psfbod & ", psftss= " & v_psftss & ", cu= " & v_cu & ", seg = " &

v_seg & " " & _

"WH ERE n pdes = '" & v_npde s & "';"

DoCmd.RunSQ L (UpdqryStr)

ttmunselect.MoveNext

Loop

ttmunselect.Close

MsgBox i & " were updated"

DoCmd .SetWarnings Fa lse

End Sub
C - 6
C - 7

Module 3: Update municipal facilities

Sub UpdateM un()

'Create 11/12/01 by Megan Tulloch

'Last modified 11/13/01 by Megan Tulloch

'Tables

Dim dbs As D atabase

Dim ttmunselect As Recordset

Dim modmunselect As Recordset

Set dbs = CurrentDb()

'Variables

Dim i As Long

Dim v_npdes As Variant

Dim v_ttflow As Variant

Dim v_modflow As Variant

Dim v_bod_new A s Variant

Dim v_tss_new As Variant

Dim v_tn_new As Variant

Dim v_tp_new As Variant

Dim v_fec_new As Variant

Dim v_flow_new As Variant

Dim v_tkn_new As Variant

Dim v_primary_BOD As Variant

Dim v_primary_TSS As V ariant

Dim v_primary_TN A s Variant

Dim v_primary_TP A s Variant

Dim v_primary_FEC As V ariant

Dim v_primary_TKN As Variant

Dim v_advprimary_BO D As Variant

Dim v_advprimary_TSS As Variant

Dim v_advprimary_TN As Variant

Dim v_advprimary_TP As Variant

Dim v_advprimary_FEC A s Variant

Dim v_advprimary_TK N As Variant

Dim v_secondary_BO D As Variant

Dim v_secondary_TSS As Variant

Dim v_secondary_TN As Variant

Dim v_secondary_TP As Variant

Dim v_secondary_FEC A s Variant

Dim v_secondary_TK N As Variant

Dim v_advwt1_BO D As Variant

Dim v_advwt1_TS S As Variant

Dim v_advwt1_TN As Variant

Dim v_advwt1_TP As Variant

Dim v_advwt1_FEC As Variant

Dim v_advwt1_TK N As Variant

Dim v_advwt2_BO D As Variant

Dim v_advwt2_TS S As Variant

Dim v_advwt2_TN As Variant

Dim v_advwt2_TP As Variant

Dim v_advwt2_FEC As Variant

Dim v_advwt2_TK N As Variant
C - 8

Dim v_default_BOD A s Variant

Dim v_default_TSS As Variant

Dim v_default_TN As V ariant

Dim v_default_TP As Variant

Dim v_default_FEC As Variant

Dim v_default_TKN As Variant

'SQL Query Statement Variables

Dim TTSelqryStr As String

Dim SelqryStr As String

Dim UpdqryStr As String

 v_primar y_BO D = 0.7

 v_primar y_TSS = 0.5

 v_primary_TN = 0.78

 v_primary_TP = 0.87

 v_primary_FEC = 0.65

 v_primary_TKN = 0.78

 v_advp rimary_B OD = 0.5

 v_advp rimary_T SS = 0.3

 v_advprimary_TN = 0.78

 v_advprimary_TP = 0.87

 v_advprimary_FEC = 0.65

 v_advprimary_TKN = 0.78

 v_secondary_BOD = 0.08

 v_secondary_TSS = 0.08

 v_secondary_TN = 0.61

 v_secondary_TP = 0.42

 v_secondary_FEC = 0.0005

 v_secondary_TKN = 0.55

 v_advwt1_BOD = 0.03

 v_advwt1_TSS = 0.03

 v_advwt1_TN = 0.61

 v_advwt1_TP = 0.06

 v_advwt1_FEC = 0.0005

 v_advwt1_TKN = 0.43

 v_advwt2_BOD = 0.02

 v_advwt2_TSS = 0.02

 v_advwt2_TN = 0.48

 v_advwt2_TP = 0.06

 v_advwt2_FEC = 0.0000032

 v_advwt2_TKN = 0.12

 v_default_BOD = 0.08

 v_default_TSS = 0.08

 v_default_TN = 0.61

 v_default_TP = 0.42

 v_default_FEC = 0.0005

 v_default_TKN = 0.55

'Open T T Mu nicipal Da ta

TTS elqryStr = " select * from T TIndire ct where op tion = 'BAC ';"

Set ttmunselect = dbs.OpenRecordset(TTSelqryStr)

DoCmd .SetWarnings Fa lse

i = 0

ttmunselect.MoveF irst
C - 9

Do Until ttmunselect.EOF

i = i + 1

'Select Row in NWPCAM Model Data corresponding TT data by NPDES

SelqryStr = "select * from munselect wh ere npde s = '" & ttmuns elect!NP DES & "';"

Set modmunselect = dbs.OpenRecord set(SelqryStr)

'Set variable from TT data to be m oved into N WPCAM Industrial Table

 v_npdes = ttmunselect!NPDES

'Set Flow variables to be used in calculating loads

 v_modflow = modmunselect!Flow

 v_ttflow = ttmunselect!Flow 'If TT flows are in gpd, need to add in conversion factor!

'Calulate new flow value to be in serted in indu strial select table

'changed from ttmunselect!Flow to v_ttflow

 v_flow_new = modmunselect!Flow - v_ttflow

'If new flow <0 then set all concentration variables to original values

If v_flow_new <= 0 Then

v_flow_n ew = mo dmunsele ct!Flow / 2

v_bod_new = modmunselect!BOD

v_tss_new = modmunselect!TSS

v_tn_new = modmunselect!TN

v_tp_new = modmunselect!TP

v_fec_new = modmunselect!FEC

v_tkn_new = modmunselect!TKN

ElseIf modmunselect!LEVEL = 2 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod_new = ((modm unselect!BOD * v_modflow) - (ttmunselect!BOD * v_primary_BOD * v_ttflow)) /

v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_primary_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_primary_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_primary_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_primary_FEC * v_ttflow)) /

v_flow_new

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_primary_TKN * v_ttflow)) /

v_flow_new

ElseIf modmunselect!LEVEL = 3 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod_new = ((modmunselect!BOD * v_modflow) - (ttmunselect!BOD * v_advprimary_BOD *

v_ttflow)) / v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_advprimary_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_advprimary_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_advprimary_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_advprimary_FEC * v_ttflow)) /

v_flow_new

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_advprimary_TKN * v_ttflow))

/ v_flow_new

ElseIf modmunselect!LEVEL = 4 Then

'Calculate load s for both T T and N WPCAM municipal d ata
C - 10

 v_bod_new = ((modm unselect!BOD * v_modflow) - (ttmunselect!BOD * v_secondary_BOD * v_ttflow))

/ v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_secondary_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_secondary_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_secondary_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_secondary_FEC * v_ttflow)) /

v_flow_new

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_secondary_TKN * v_ttflow)) /

v_flow_new

ElseIf modmunselect!LEVEL = 5 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod_new = ((modm unselect!BOD * v_modflow) - (ttmunselect!BOD * v_advwt1_BO D * v_ttflow)) /

v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_advwt1_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_advwt1_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_advwt1_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_advwt1_FEC * v_ttflow)) /

v_flow_new

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_advwt1_TKN * v_ttflow)) /

v_flow_new

ElseIf modmunselect!LEVEL = 6 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod_new = ((modm unselect!BOD * v_modflow) - (ttmunselect!BOD * v_advwt2_BO D * v_ttflow)) /

v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_advwt2_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_advwt2_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_advwt2_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_advwt2_FEC * v_ttflow)) /

v_flow_new

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_advwt2_TKN * v_ttflow)) /

v_flow_new

ElseIf modmunselect!LEVEL = 9 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod_new = ((modm unselect!BOD * v_modflow) - (ttmunselect!BOD * v_default_BOD * v_ttflow)) /

v_flow_new

 v_tss_new = ((modmunselect!TSS * v_modflow) - (ttmunselect!TSS * v_default_TSS * v_ttflow)) /

v_flow_new

 v_tn_new = ((modmunselect!TN * v_modflow) - (ttmunselect!TN * v_default_TN * v_ttflow)) /

v_flow_new

 v_tp_new = ((modmunselect!TP * v_mod flow) - (ttmunselect!TP * v_default_TP * v_ttflow)) /

v_flow_new

 v_fec_new = ((modmunselect!FEC * v_modflow) - (ttmunselect!FEC * v_default_FEC * v_ttflow)) /

v_flow_new
C - 11

 v_tkn_new = ((modmunselect!TKN * v_m odflow) - (ttmunselect!TKN * v_default_TKN * v_ttflow)) /

v_flow_new

End If

'QA new co ncentrations to see if any are < 0. If they are, set to d efault values ba sed on trea tment level.

If v_bod_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_bod_ new = 14 3.5

 ElseIf modmunselect!LEVEL = 3 Then

 v_bod_ new = 10 2.5

 ElseIf modmunselect!LEVEL = 4 Then

 v_bod_ new = 16 .4

ElseIf modmunselect!LEVEL = 5 Then

 v_bod_ new = 6.2

 ElseIf modmunselect!LEVEL = 6 Then

 v_bod_ new = 4.1

 ElseIf modmunselect!LEVEL = 9 Then

 v_bod_ new = 16 .4

 End If

End If

If v_tss_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_tss_new = 107.5

 ElseIf modmunselect!LEVEL = 3 Then

 v_tss_new = 64.5

 ElseIf modmunselect!LEVEL = 4 Then

 v_tss_new = 17.2

 ElseIf modmunselect!LEVEL = 5 Then

 v_tss_new = 6.5

 ElseIf modmunselect!LEVEL = 6 Then

 v_tss_new = 4.3

 ElseIf modmunselect!LEVEL = 9 Then

 v_tss_new = 17.2

 End If

End If

If v_tn_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_tn_new = 23.4

 ElseIf modmunselect!LEVEL = 3 Then

 v_tn_new = 23.4

 ElseIf modmunselect!LEVEL = 4 Then

 v_tn_new = 18.3

 ElseIf modmunselect!LEVEL = 5 Then

 v_tn_new = 18.4

 ElseIf modmunselect!LEVEL = 6 Then

 v_tn_new = 14.4

 ElseIf modmunselect!LEVEL = 9 Then

 v_tn_new = 18.3

 End If

End If

If v_tp_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_tp_new = 5.2
C - 12

 ElseIf modmunselect!LEVEL = 3 Then

 v_tp_new = 5.2

 ElseIf modmunselect!LEVEL = 4 Then

 v_tp_new = 2.5

 ElseIf modmunselect!LEVEL = 5 Then

 v_tp_new = 0.4

 ElseIf modmunselect!LEVEL = 6 Then

 v_tp_new = 0.4

 ElseIf modmunselect!LEVEL = 9 Then

 v_tp_new = 2.5

 End If

End If

If v_fec_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_fec_new = 2060000

 ElseIf modmunselect!LEVEL = 3 Then

 v_fec_new = 2060000

 ElseIf modmunselect!LEVEL = 4 Then

 v_fec_new = 1580

 ElseIf modmunselect!LEVEL = 5 Then

 v_fec_new = 1580

 ElseIf modmunselect!LEVEL = 6 Then

 v_fec_new = 10

 ElseIf modmunselect!LEVEL = 9 Then

 v_fec_new = 1580

 End If

End If

If v_tkn_new <= 0 Then

 If modmunselect!LEVEL = 2 Then

 v_tkn_new = 23.4

 ElseIf modmunselect!LEVEL = 3 Then

 v_tkn_new = 23.4

 ElseIf modmunselect!LEVEL = 4 Then

 v_tkn_new = 16.5

 ElseIf modmunselect!LEVEL = 5 Then

 v_tkn_new = 12.9

 ElseIf modmunselect!LEVEL = 6 Then

 v_tkn_new = 3.6

 ElseIf modmunselect!LEVEL = 9 Then

 v_tkn_new = 16.5

 End If

End If

‘Update the new row with the constan t data to cop ied from the NWPCAM municipal se lect table

UpdqryStr = "UPDATE munselect SET " & _

"flow = " & v_flow_new & ", BOD = '" & v_bod_new & "', TSS = " & v_tss_new & ", tn = " & v_tn_new

& ", TP= " & v_tp_new & ", FEC= " & v_fec_new & ", TKN= " & v_tkn_new & " " & _

"WH ERE n pdes = '" & v_npde s & "';"

DoCmd.RunSQ L (UpdqryStr)

ttmunselect.MoveNext

Loop

ttmunselect.Close
C - 13

MsgBox i & " were updated"

DoCmd .SetWarnings Fa lse

End Sub

Module 4: Update indirect facilities

Sub UpdateTT Mun()

'Created 12/5/01 by Amy Miles

'Last modified 1/9/02 by Amy Miles

'Tables

Dim dbs As D atabase

Dim ttmunselect As Recordset

Dim modmunselect As Recordset

Set dbs = CurrentDb()

'Variables

Dim i As Long

Dim v_npdes As Variant

Dim v_type As Variant

Dim v_bod As V ariant

Dim v_tss As Variant

Dim v_tn As Variant

Dim v_tp As Variant

Dim v_fec As Variant

Dim v_tkn As Variant

Dim v_ttflow

Dim v_primary_BOD As Variant

Dim v_primary_TSS As V ariant

Dim v_primary_TN A s Variant

Dim v_primary_TP A s Variant

Dim v_primary_FEC As V ariant

Dim v_primary_TKN As Variant

Dim v_advprimary_BO D As Variant

Dim v_advprimary_TSS As Variant

Dim v_advprimary_TN As Variant

Dim v_advprimary_TP As Variant

Dim v_advprimary_FEC A s Variant

Dim v_advprimary_TK N As Variant

Dim v_secondary_BO D As Variant

Dim v_secondary_TSS As Variant

Dim v_secondary_TN As Variant

Dim v_secondary_TP As Variant

Dim v_secondary_FEC A s Variant

Dim v_secondary_TK N As Variant

Dim v_advwt1_BO D As Variant

Dim v_advwt1_TS S As Variant

Dim v_advwt1_TN As Variant

Dim v_advwt1_TP As Variant

Dim v_advwt1_FEC As Variant

Dim v_advwt1_TK N As Variant

Dim v_advwt2_BO D As Variant
C - 14

Dim v_advwt2_TS S As Variant

Dim v_advwt2_TN As Variant

Dim v_advwt2_TP As Variant

Dim v_advwt2_FEC As Variant

Dim v_advwt2_TK N As Variant

Dim v_default_BOD A s Variant

Dim v_default_TSS As Variant

Dim v_default_TN As V ariant

Dim v_default_TP As Variant

Dim v_default_FEC As Variant

Dim v_default_TKN As Variant

Dim v_seqno As Variant

Dim v_mi As Variant

Dim v_do As Variant

Dim v_cbodtoubod As Variant

Dim v_psfbod As Variant

Dim v_psftss As Variant

Dim v_cu As Variant

Dim v_seg As Variant

Dim v_name As Variant

 v_primar y_BO D = 0.7

 v_primar y_TSS = 0.5

 v_primary_TN = 0.78

 v_primary_TP = 0.87

 v_primary_FEC = 0.65

 v_primary_TKN = 0.78

 v_advp rimary_B OD = 0.5

 v_advp rimary_T SS = 0.3

 v_advprimary_TN = 0.78

 v_advprimary_TP = 0.87

 v_advprimary_FEC = 0.65

 v_advprimary_TKN = 0.78

 v_secondary_BOD = 0.08

 v_secondary_TSS = 0.08

 v_secondary_TN = 0.61

 v_secondary_TP = 0.42

 v_secondary_FEC = 0.0005

 v_secondary_TKN = 0.55

 v_advwt1_BOD = 0.03

 v_advwt1_TSS = 0.03

 v_advwt1_TN = 0.61

 v_advwt1_TP = 0.06

 v_advwt1_FEC = 0.0005

 v_advwt1_TKN = 0.43

 v_advwt2_BOD = 0.02

 v_advwt2_TSS = 0.02

 v_advwt2_TN = 0.48

 v_advwt2_TP = 0.06

 v_advwt2_FEC = 0.0000032

 v_advwt2_TKN = 0.12

 v_default_BOD = 0.08

 v_default_TSS = 0.08
C - 15

 v_default_TN = 0.61

 v_default_TP = 0.42

 v_default_FEC = 0.0005

 v_default_TKN = 0.55

'SQL query statement variables

Dim TTSelqryStr As String

Dim UpdqryStr As String

Dim SelqryStr As String

'Open T T Mu nicipal Da ta

TTS elqryStr = " select * from T TIndire ct where (op tion = 'BAT 4' Or optio n = 'PSES 1');"

Set ttmunselect = dbs.OpenRecordset(TTSelqryStr)

DoCmd .SetWarnings Fa lse

i = 0

ttmunselect.MoveF irst

Do Until ttmunselect.EOF

i = i + 1

'Select Row in NWPCA M Model Data corresponding to TT data by NPDES number

SelqryStr = "select * from munselect wh ere npde s = '" & ttmuns elect!NP DES & "';"

Set modmunselect = dbs.OpenRecord set(SelqryStr)

'Set variable from TT data to be m oved into N WPCAM Industrial Table

 v_npdes = ttmunselect!NPDES

 v_type = "INDIRECT"

'Set Flow variables to be used in calculating loads

 v_ttflow = ttmunselect!Flow 'if TT flows are in gpd, need to add conversion factor

If modmunselect!LEVEL = 2 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_primary_BOD

 v_tss = ttmunselect!TSS * v_primary_TSS

 v_tn = ttmunselect!TN * v_primary_TN

 v_tp = ttmunselect!TP * v_primary_TP

 v_fec = ttmunselect!FEC * v_primary_FEC

 v_tkn = ttmunselect!TKN * v_primary_TKN

ElseIf modmunselect!LEVEL = 3 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advprimary_BOD

 v_tss = ttmunselect!TSS * v_advprimary_TSS

 v_tn = ttmunselect!TN * v_advprimary_TN

 v_tp = ttmunselect!TP * v_advprimary_TP

 v_fec = ttmunselect!FEC * v_advprimary_FEC

 v_tkn = ttmunselect!TKN * v_advprimary_TKN

ElseIf modmunselect!LEVEL = 4 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_secondary_BOD

 v_tss = ttmunselect!TSS * v_secondary_TSS

 v_tn = ttmunselect!TN * v_secondary_TN

 v_tp = ttmunselect!TP * v_secondary_TP

 v_fec = ttmunselect!FEC * v_secondary_FEC

 v_tkn = ttmunselect!TKN * v_secondary_TKN

ElseIf modmunselect!LEVEL = 5 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advwt1_BOD

 v_tss = ttmunselect!TSS * v_advwt1_TSS
C - 16

 v_tn = ttmunselect!TN * v_advwt1_TN

 v_tp = ttmunselect!TP * v_advwt1_TP

 v_fec = ttmunselect!FEC * v_advwt1_FEC

 v_tkn = ttmunselect!TKN * v_advwt1_TKN

ElseIf modmunselect!LEVEL = 6 Then

'Calculate load s for both T T and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_advwt2_BOD

 v_tss = ttmunselect!TSS * v_advwt2_TSS

 v_tn = ttmunselect!TN * v_advwt2_TN

 v_tp = ttmunselect!TP * v_advwt2_TP

 v_fec = ttmunselect!FEC * v_advwt2_FEC

 v_tkn = ttmunselect!TKN * v_advwt2_TKN

 ElseIf modmunselect!LEVEL = 9 Then

'Calculate con centrations fo r both TT and N WPCAM municipal d ata

 v_bod = ttmunselect!BOD * v_default_BOD

 v_tss = ttmunselect!TSS * v_default_TSS

 v_tn = ttmunselect!TN * v_default_TN

 v_tp = ttmunselect!TP * v_default_TP

 v_fec = ttmunselect!FEC * v_default_FEC

 v_tkn = ttmunselect!TKN * v_default_TKN

 End If

 'Update NWPCAM industrial select table with TT values by corresponding NPDES

UpdqryStr = "UPDATE indselect SET " & _

"flow = " & v_ttflow & ", bod = " & v_bod & ", tss= " & v_tss & ", tn= " & v_tn & ", tp= " & v_tp & ",

fec= " & v_fec & " , tkn= " & v_tkn & " " & _

"WH ERE n pdes = '" & v_npde s & "';"

DoCmd.RunSQ L (UpdqryStr)

ttmunselect.MoveNext

Loop

ttmunselect.Close

MsgBox i & " were updated"

DoCmd .SetWarnings Fa lse

End Sub